From ea63903f920af8e65da697425b6b08432619f429 Mon Sep 17 00:00:00 2001
From: szcf-weiya <szcf-weiya@users.noreply.github.com>
Date: Sun, 28 Apr 2024 02:54:00 +0000
Subject: [PATCH] deploy: 9600d273a3b953951217ffbcf65c72c6f5cac9fc

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 <ul>
 <li>activation: 激活 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
 <li>ability to classify: 判别能力 (<a href="../04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis/index.html">第 4.3 节</a>)</li>
-<li>analysis of deviance: 偏差分析 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>adjusted response: 调整后的响应变量 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
+<li>analysis of deviance: 偏差分析 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>autoregressive: 自回归 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
 <li>aorta: 大动脉 (<a href="../06-Kernel-Smoothing-Methods/6.4-Structured-Local-Regression-Models-in-Rp/index.html">第 6.4 节</a>)</li>
 <li>automatic kernel carpentry: 自动核作品 (<a href="../06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers/index.html">第 6.1 节</a>)</li>
@@ -3538,8 +3538,8 @@ <h2 id="a">A<a class="headerlink" href="#a" title="Permanent link">&para;</a></h
 <li>alternate: 轮换 (<a href="../08-Model-Inference-and-Averaging/8.5-The-EM-Algorithm/index.html">第 8.5 节</a>)</li>
 <li>alternative maximization procedure: 轮换最大化过程 (<a href="../08-Model-Inference-and-Averaging/8.5-The-EM-Algorithm/index.html">第 8.5 节</a>)</li>
 <li>automatic flexible: 自动灵活 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.1-Generalized-Additive-Models/index.html">第 9.1 节</a>)</li>
-<li>aggregated data: 聚合数据 (<a href="../10-Boosting-and-Additive-Trees/10.14-Illustrations/index.html">第 10.14 节</a>)</li>
 <li>abundance: 多度 (<a href="../10-Boosting-and-Additive-Trees/10.14-Illustrations/index.html">第 10.14 节</a>)</li>
+<li>aggregated data: 聚合数据 (<a href="../10-Boosting-and-Additive-Trees/10.14-Illustrations/index.html">第 10.14 节</a>)</li>
 <li>additive expansions: 加性展开 (<a href="../10-Boosting-and-Additive-Trees/10.2-Boosting-Fits-an-Additive-Model/index.html">第 10.2 节</a>)</li>
 <li>ad-hoc: 特定 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.2-Prototype-Methods/index.html">第 13.2 节</a>)</li>
 <li>a collection of objects: 点集 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
@@ -3547,9 +3547,9 @@ <h2 id="a">A<a class="headerlink" href="#a" title="Permanent link">&para;</a></h
 <li>Association rule analysis: 关联规则分析 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
 <li>adjacency matrix: 邻接矩阵 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>arc length: 角长度 (<a href="../16-Ensemble-Learning/16.2-Boosting-and-Regularization-Paths/index.html">第 16.2 节</a>)</li>
-<li>associated probability distribution: 相关概率分布 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
-<li>affinities: 亲和力 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 <li>adjacent: 邻接 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
+<li>affinities: 亲和力 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
+<li>associated probability distribution: 相关概率分布 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 </ul>
 <h2 id="b">B<a class="headerlink" href="#b" title="Permanent link">&para;</a></h2>
 <ul>
@@ -3565,9 +3565,9 @@ <h2 id="b">B<a class="headerlink" href="#b" title="Permanent link">&para;</a></h
 <li>BIC: 贝叶斯信息准则 (<a href="../07-Model-Assessment-and-Selection/7.7-The-Bayesian-Approach-and-BIC/index.html">第 7.7 节</a>)</li>
 <li>basis set: 基础集 (<a href="../07-Model-Assessment-and-Selection/7.6-The-Effective-Number-of-Parameters/index.html">第 7.6 节</a>)</li>
 <li>burn-in: 老化 (<a href="../08-Model-Inference-and-Averaging/8.6-MCMC-for-Sampling-from-the-Posterior/index.html">第 8.6 节</a>)</li>
-<li>Bootstrap aggregation: 自助法整合 (<a href="../08-Model-Inference-and-Averaging/8.7-Bagging/index.html">第 8.7 节</a>)</li>
 <li>bag: 打包 (<a href="../08-Model-Inference-and-Averaging/8.7-Bagging/index.html">第 8.7 节</a>)</li>
 <li>bootstrap: 自助法 (<a href="../08-Model-Inference-and-Averaging/8.7-Bagging/index.html">第 8.7 节</a>)</li>
+<li>Bootstrap aggregation: 自助法整合 (<a href="../08-Model-Inference-and-Averaging/8.7-Bagging/index.html">第 8.7 节</a>)</li>
 <li>Bay Area: 海湾地区 (<a href="../10-Boosting-and-Additive-Trees/10.14-Illustrations/index.html">第 10.14 节</a>)</li>
 <li>black box: 黑箱方法 (<a href="../10-Boosting-and-Additive-Trees/10.7-Off-the-Shelf-Procedures-for-Data-Mining/index.html">第 10.7 节</a>)</li>
 <li>back-propagation equations: 向后传播等式 (<a href="../11-Neural-Networks/11.4-Fitting-Neural-Networks/index.html">第 11.4 节</a>)</li>
@@ -3576,8 +3576,8 @@ <h2 id="b">B<a class="headerlink" href="#b" title="Permanent link">&para;</a></h
 <li>blurred: 模糊的 (<a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.7-Mixture-Discriminant-Analysis/index.html">第 12.7 节</a>)</li>
 <li>branch-and-bound combinatorial method: 分枝定界组合算法 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>bump hunting: 碰撞狩猎 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
-<li>blind source separation: 盲信号分离 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>brain dynamics: 脑动力学 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
+<li>blind source separation: 盲信号分离 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>Bayesian networks: 贝叶斯网络 (<a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>)</li>
 <li>Boltzmann machines: 玻尔兹曼机 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 </ul>
@@ -3586,8 +3586,8 @@ <h2 id="c">C<a class="headerlink" href="#c" title="Permanent link">&para;</a></h
 <li>complexity: 复杂性 (<a href="../02-Overview-of-Supervised-Learning/2.9-Model-Selection-and-the-Bias-Variance-Tradeoff/index.html">第 2.9 节</a>)</li>
 <li>categories: 类别型 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>classification: 分类 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>, <a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.1-Introduction/index.html">第 13.1 节</a>)</li>
-<li>closed: 闭型 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
 <li>cross-entropy: 交叉熵 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>, <a href="../09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods/index.html">第 9.2 节</a>)</li>
+<li>closed: 闭型 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
 <li>curse of dimensionality: 维度的诅咒 (<a href="../02-Overview-of-Supervised-Learning/2.5-Local-Methods-in-High-Dimensions/index.html">第 2.5 节</a>)</li>
 <li>cubic smoothing spline: 三次光滑样条 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
 <li>compressed sensing: 压缩传感 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
@@ -3597,9 +3597,9 @@ <h2 id="c">C<a class="headerlink" href="#c" title="Permanent link">&para;</a></h
 <li>cut-point: 分隔点 (<a href="../04-Linear-Methods-for-Classification/4.1-Introduction/index.html">第 4.1 节</a>)</li>
 <li>canonical variables: 典则变量 (<a href="../04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis/index.html">第 4.3 节</a>)</li>
 <li>canonical: 典则 (<a href="../04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis/index.html">第 4.3 节</a>)</li>
-<li>cases: 案例集 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
-<li>controls: 控制集 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>concave: 凹的 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
+<li>controls: 控制集 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
+<li>cases: 案例集 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>compact kernel: 紧核 (<a href="../06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers/index.html">第 6.1 节</a>)</li>
 <li>Committee methods: 委员会方法 (<a href="../08-Model-Inference-and-Averaging/8.8-Model-Averaging-and-Stacking/index.html">第 8.8 节</a>)</li>
 <li>consensus vote: 共识投票 (<a href="../08-Model-Inference-and-Averaging/8.7-Bagging/index.html">第 8.7 节</a>)</li>
@@ -3610,28 +3610,28 @@ <h2 id="c">C<a class="headerlink" href="#c" title="Permanent link">&para;</a></h
 <li>computationally intensive: 计算密集型的 (<a href="../10-Boosting-and-Additive-Trees/10.2-Boosting-Fits-an-Additive-Model/index.html">第 10.2 节</a>)</li>
 <li>condensing: 压缩 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.5-Computational-Considerations/index.html">第 13.5 节</a>)</li>
 <li>checkerboard: 跳棋盘 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.3-k-Nearest-Neighbor-Classifiers/index.html">第 13.3 节</a>)</li>
-<li>combinatorial algorithms: 组合算法 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
-<li>Complete linkage, CL: 全链接 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>cophenetic correlation coefficient: 共表型相关系数 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>cluster analysis: 聚类分析 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
+<li>combinatorial algorithms: 组合算法 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>codebook: 码本 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
+<li>Complete linkage, CL: 全链接 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>coordinate descent: 坐标下降 (<a href="../14-Unsupervised-Learning/14.9-Nonlinear-Dimension-Reduction-and-Local-Multidimensional-Scaling/index.html">第 14.9 节</a>)</li>
 <li>cardinality: 基数 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
-<li>confidence: 置信度 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
 <li>conjunctive rule: 联合规则 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
+<li>confidence: 置信度 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
 <li>classical scaling: 经典缩放 (<a href="../14-Unsupervised-Learning/14.8-Multidimensional-Scaling/index.html">第 14.8 节</a>)</li>
-<li>correlated: 相关的 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>cardiac pulse: 心脏脉冲 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
+<li>correlated: 相关的 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>constrained topological map: 约束拓扑图 (<a href="../14-Unsupervised-Learning/14.4-Self-Organizing-Maps/index.html">第 14.4 节</a>)</li>
 <li>convex hull: 凸包 (<a href="../14-Unsupervised-Learning/14.6-Non-negative-Matrix-Factorization/index.html">第 14.6 节</a>)</li>
 <li>complete graph: 完全图 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
-<li>clique potentials: 团势 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 <li>clique: 团 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
+<li>clique potentials: 团势 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 <li>covariance: 协方差图 (<a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>)</li>
-<li>contrastive divergence, CD: 对比发散 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
-<li>contigency table: 列联表 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
-<li>constant node: 常值结点 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 <li>cyclical coordinate descent: 循环坐标下降 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
+<li>constant node: 常值结点 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
+<li>contigency table: 列联表 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
+<li>contrastive divergence, CD: 对比发散 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 <li>cyclical coordinate descent: 坐标轮换 (<a href="../18-High-Dimensional-Problems/18.4-Linear-Classifiers-with-L1-Regularization/index.html">第 18.4 节</a>)</li>
 </ul>
 <h2 id="d">D<a class="headerlink" href="#d" title="Permanent link">&para;</a></h2>
@@ -3641,15 +3641,15 @@ <h2 id="d">D<a class="headerlink" href="#d" title="Permanent link">&para;</a></h
 <li>dependent variables: 因变量 (<a href="../02-Overview-of-Supervised-Learning/2.1-Introduction/index.html">第 2.1 节</a>)</li>
 <li>dictionary: 字典 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
 <li>diamond: 菱形 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
-<li>discriminant functions: 判别函数 (<a href="../04-Linear-Methods-for-Classification/4.1-Introduction/index.html">第 4.1 节</a>)</li>
 <li>decision boundaries: 线性判别边界 (<a href="../04-Linear-Methods-for-Classification/4.1-Introduction/index.html">第 4.1 节</a>)</li>
+<li>discriminant functions: 判别函数 (<a href="../04-Linear-Methods-for-Classification/4.1-Introduction/index.html">第 4.1 节</a>)</li>
 <li>discriminant: 判别 (<a href="../04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis/index.html">第 4.3 节</a>)</li>
 <li>discriminant variable: 判别变量 (<a href="../04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis/index.html">第 4.3 节</a>)</li>
 <li>discriminant coordinates: 判别坐标 (<a href="../04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis/index.html">第 4.3 节</a>)</li>
 <li>down-weighted: 低估 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
+<li>dilation: 伸缩 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>detail: 细节 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>dictionaries of basis functions: 基函数的字典集 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
-<li>dilation: 伸缩 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>driven: 驱动 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>divided differences: 差商 (<a href="../05-Basis-Expansions-and-Regularization/Appendix-Computations-for-B-splines/index.html">第 Appendix 节</a>)</li>
 <li>deviance: 偏差 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>, <a href="../10-Boosting-and-Additive-Trees/10.5-Why-Exponential-Loss/index.html">第 10.5 节</a>)</li>
@@ -3658,10 +3658,10 @@ <h2 id="d">D<a class="headerlink" href="#d" title="Permanent link">&para;</a></h
 <li>derived features: 导出特征 (<a href="../11-Neural-Networks/11.1-Introduction/index.html">第 11.1 节</a>)</li>
 <li>digitized analog signals: 数字化模拟信号 (<a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.6-Penalized-Discriminant-Analysis/index.html">第 12.6 节</a>)</li>
 <li>DANN: 判别自适应最近邻 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.4-Adaptive-Nearest-Neighbor-Methods/index.html">第 13.4 节</a>)</li>
-<li>dendrogram: 谱系图 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
-<li>data segmentation: 数据分离 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>decoding: 解码 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>dissimilarities: 不相似性 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
+<li>dendrogram: 谱系图 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
+<li>data segmentation: 数据分离 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>disjunction: 并集 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
 <li>degree: 度 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>digitized: 数字化的 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
@@ -3672,10 +3672,10 @@ <h2 id="d">D<a class="headerlink" href="#d" title="Permanent link">&para;</a></h
 <li>directed graphical models: 有向图 (<a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>)</li>
 <li>decomposable models: 可分解模型 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 <li>double centered: 二次中心化 (<a href="../18-High-Dimensional-Problems/18.5-Classification-When-Features-are-Unavailable/index.html">第 18.5 节</a>)</li>
-<li>discriminant score: 判别得分 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
 <li>diagonal-covariance: 对角协方差 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
-<li>difference penalty: 差异惩罚 (<a href="../18-High-Dimensional-Problems/18.4-Linear-Classifiers-with-L1-Regularization/index.html">第 18.4 节</a>)</li>
+<li>discriminant score: 判别得分 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
 <li>divided differences: 划分差异 (<a href="../18-High-Dimensional-Problems/18.4-Linear-Classifiers-with-L1-Regularization/index.html">第 18.4 节</a>)</li>
+<li>difference penalty: 差异惩罚 (<a href="../18-High-Dimensional-Problems/18.4-Linear-Classifiers-with-L1-Regularization/index.html">第 18.4 节</a>)</li>
 </ul>
 <h2 id="e">E<a class="headerlink" href="#e" title="Permanent link">&para;</a></h2>
 <ul>
@@ -3686,15 +3686,15 @@ <h2 id="e">E<a class="headerlink" href="#e" title="Permanent link">&para;</a></h
 <li>effect size: 有效大小 (<a href="../03-Linear-Methods-for-Regression/3.2-Linear-Regression-Models-and-Least-Squares/index.html">第 3.2 节</a>)</li>
 <li>EPE: 积分平方预测误差 (<a href="../05-Basis-Expansions-and-Regularization/5.5-Automatic-Selection-of-the-Smoothing-Parameters/index.html">第 5.5 节</a>)</li>
 <li>equivalent kernels: 等价核 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
-<li>eigen-expansion: 特征展开 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>eigen-functions: 特征函数 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
+<li>eigen-expansion: 特征展开 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>extrapolation: 向外推断 (<a href="../06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers/index.html">第 6.1 节</a>)</li>
 <li>evaluation: 赋值 (<a href="../06-Kernel-Smoothing-Methods/6.0-Introduction/index.html">第 6.0 节</a>)</li>
 <li>expected test error: 期望测试误差 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
 <li>expected prediction error: 期望预测误差 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
 <li>estimation bias: 估计偏差 (<a href="../07-Model-Assessment-and-Selection/7.3-The-Bias-Variance-Decomposition/index.html">第 7.3 节</a>)</li>
-<li>effective number of parameters: 有效参数个数 (<a href="../07-Model-Assessment-and-Selection/7.4-Optimism-of-the-Training-Error-Rate/index.html">第 7.4 节</a>, <a href="../07-Model-Assessment-and-Selection/7.6-The-Effective-Number-of-Parameters/index.html">第 7.6 节</a>)</li>
 <li>expected error: 期望误差 (<a href="../07-Model-Assessment-and-Selection/7.4-Optimism-of-the-Training-Error-Rate/index.html">第 7.4 节</a>)</li>
+<li>effective number of parameters: 有效参数个数 (<a href="../07-Model-Assessment-and-Selection/7.4-Optimism-of-the-Training-Error-Rate/index.html">第 7.4 节</a>, <a href="../07-Model-Assessment-and-Selection/7.6-The-Effective-Number-of-Parameters/index.html">第 7.6 节</a>)</li>
 <li>extra-sample: 样本外 (<a href="../07-Model-Assessment-and-Selection/7.4-Optimism-of-the-Training-Error-Rate/index.html">第 7.4 节</a>, <a href="../07-Model-Assessment-and-Selection/7.10-Cross-Validation/index.html">第 7.10 节</a>)</li>
 <li>extra-sample error: 样本外误差 (<a href="../07-Model-Assessment-and-Selection/7.4-Optimism-of-the-Training-Error-Rate/index.html">第 7.4 节</a>)</li>
 <li>extra-sample prediction error: 样本外预测误差 (<a href="../07-Model-Assessment-and-Selection/7.11-Bootstrap-Methods/index.html">第 7.11 节</a>)</li>
@@ -3703,8 +3703,8 @@ <h2 id="e">E<a class="headerlink" href="#e" title="Permanent link">&para;</a></h
 <li>experts: 专家 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
 <li>early stopping: 早停 (<a href="../10-Boosting-and-Additive-Trees/10.12-Regularization/index.html">第 10.12 节</a>)</li>
 <li>empirical risk: 经验风险 (<a href="../10-Boosting-and-Additive-Trees/10.9-Boosting-Trees/index.html">第 10.9 节</a>)</li>
-<li>exterior point: 外点 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.5-Computational-Considerations/index.html">第 13.5 节</a>)</li>
 <li>editing: 编辑 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.5-Computational-Considerations/index.html">第 13.5 节</a>)</li>
+<li>exterior point: 外点 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.5-Computational-Considerations/index.html">第 13.5 节</a>)</li>
 <li>encoding: 编码 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>effectiveness: 有效性 (<a href="../14-Unsupervised-Learning/14.1-Introduction/index.html">第 14.1 节</a>)</li>
 <li>exploratory projection pursuit: 探索投影寻踪 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
@@ -3718,12 +3718,12 @@ <h2 id="f">F<a class="headerlink" href="#f" title="Permanent link">&para;</a></h
 <li>factors: 因子 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>features: 特征 (<a href="../02-Overview-of-Supervised-Learning/2.1-Introduction/index.html">第 2.1 节</a>)</li>
 <li>Forward-stepwise selection: 向前逐步选择 (<a href="../03-Linear-Methods-for-Regression/3.3-Subset-Selection/index.html">第 3.3 节</a>)</li>
+<li>forward-stagewise regression: 向前逐渐回归 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
 <li>FS: 向前逐渐过程 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
 <li>Forward Stagewise Regression: 向前逐渐回归 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
-<li>forward-stagewise regression: 向前逐渐回归 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
 <li>fancier: 更花哨的 (<a href="../03-Linear-Methods-for-Regression/3.1-Introduction/index.html">第 3.1 节</a>)</li>
-<li>filtered: 滤波 (<a href="../05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines/index.html">第 5.2 节</a>)</li>
 <li>functional: 函数型 (<a href="../05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines/index.html">第 5.2 节</a>)</li>
+<li>filtered: 滤波 (<a href="../05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines/index.html">第 5.2 节</a>)</li>
 <li>functional linear model: 函数型线性模型 (<a href="../05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines/index.html">第 5.2 节</a>)</li>
 <li>FFT: 快速傅里叶变换 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>filling the valleys: 填充山谷 (<a href="../06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers/index.html">第 6.1 节</a>)</li>
@@ -3731,8 +3731,8 @@ <h2 id="f">F<a class="headerlink" href="#f" title="Permanent link">&para;</a></h
 <li>field: 领域 (<a href="../11-Neural-Networks/11.4-Fitting-Neural-Networks/index.html">第 11.4 节</a>)</li>
 <li>flexible discriminant analysis: 可变的判别分析 (<a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.1-Introduction/index.html">第 12.1 节</a>)</li>
 <li>feature vector: 特征向量 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
-<li>factor loadings: 因子载荷 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>Factor Analysis: 因子分析 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
+<li>factor loadings: 因子载荷 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>first-order-interaction Poisson log-linear model: 一阶交叉泊松对数线性模型 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 <li>family-wise error rate: FWER (<a href="../18-High-Dimensional-Problems/18.7-Feature-Assessment-and-the-Multiple-Testing-Problem/index.html">第 18.7 节</a>)</li>
 <li>feature selection: 特征选择 (<a href="../18-High-Dimensional-Problems/18.1-When-p-is-Much-Bigger-than-N/index.html">第 18.1 节</a>)</li>
@@ -3763,13 +3763,13 @@ <h2 id="h">H<a class="headerlink" href="#h" title="Permanent link">&para;</a></h
 <li>high scale: 尺度最高 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>hat matrix: 帽子矩阵 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
 <li>hybrid approaches: 混合方法 (<a href="../05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines/index.html">第 5.7 节</a>)</li>
-<li>having no say: 不起作用 (<a href="../06-Kernel-Smoothing-Methods/6.7-Radial-Basis-Functions-and-Kernels/index.html">第 6.7 节</a>)</li>
 <li>holes: 洞 (<a href="../06-Kernel-Smoothing-Methods/6.7-Radial-Basis-Functions-and-Kernels/index.html">第 6.7 节</a>)</li>
+<li>having no say: 不起作用 (<a href="../06-Kernel-Smoothing-Methods/6.7-Radial-Basis-Functions-and-Kernels/index.html">第 6.7 节</a>)</li>
 <li>hierarchical mixtures of experts, HME: 专家系统混合模型 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods/index.html">第 9.2 节</a>)</li>
 <li>HME: 混合层次专家 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.0-Introduction/index.html">第 9.0 节</a>)</li>
-<li>HME: 专家的分层混合 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
 <li>hierarchical mixtures of experts: 专家的分层混合 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
 <li>hard decision: 硬决定 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
+<li>HME: 专家的分层混合 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
 <li>highly skewed: 高偏的 (<a href="../10-Boosting-and-Additive-Trees/10.7-Off-the-Shelf-Procedures-for-Data-Mining/index.html">第 10.7 节</a>)</li>
 <li>hidden units: 隐藏层 (<a href="../11-Neural-Networks/11.3-Neural-Networks/index.html">第 11.3 节</a>)</li>
 <li>hierarchical basis: 分层基 (<a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.3-Support-Vector-Machines-and-Kernels/index.html">第 12.3 节</a>)</li>
@@ -3789,8 +3789,8 @@ <h2 id="i">I<a class="headerlink" href="#i" title="Permanent link">&para;</a></h
 <li>infinitesimal forward stagewise regression: 无穷小的向前逐渐回归 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
 <li>indicator response matrix: 指示变量响应矩阵 (<a href="../04-Linear-Methods-for-Classification/4.2-Linear-Regression-of-an-Indicator-Matrix/index.html">第 4.2 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.5-Flexible-Disciminant-Analysis/index.html">第 12.5 节</a>)</li>
 <li>iteratively reweighted least squares: 加权迭代最小二乘 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
-<li>inverse wavelet transform: 逆小波变换 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>isolated bumps: 独立凹凸块 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
+<li>inverse wavelet transform: 逆小波变换 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>induced norm: 导出范数 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>iterative backfitting: 迭代向后拟合 (<a href="../06-Kernel-Smoothing-Methods/6.4-Structured-Local-Regression-Models-in-Rp/index.html">第 6.4 节</a>)</li>
 <li>interval scale: 定距型 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
@@ -3822,10 +3822,10 @@ <h2 id="j">J<a class="headerlink" href="#j" title="Permanent link">&para;</a></h
 <h2 id="k">K<a class="headerlink" href="#k" title="Permanent link">&para;</a></h2>
 <ul>
 <li>k-nearest-neighbor averaging: k-最近邻平均 (<a href="../02-Overview-of-Supervised-Learning/2.3-Two-Simple-Approaches-to-Prediction/index.html">第 2.3 节</a>)</li>
-<li>kernel function: 核函数 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
 <li>knots: 结点 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
-<li>kernel property: 核性质 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
+<li>kernel function: 核函数 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
 <li>kernel reproducing property: 核再生性质 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
+<li>kernel property: 核性质 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>kernel: 核 (<a href="../06-Kernel-Smoothing-Methods/6.0-Introduction/index.html">第 6.0 节</a>)</li>
 <li>knot: 结点 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.4-MARS/index.html">第 9.4 节</a>)</li>
 <li>kernel principal components: 核主成分 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
@@ -3838,18 +3838,18 @@ <h2 id="l">L<a class="headerlink" href="#l" title="Permanent link">&para;</a></h
 <li>large: 大 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>learning by example: 样本学习 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
 <li>linear basis expansions: 线性基展开 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
-<li>LAR: 最小角回归 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
 <li>Lagrangian form: 拉格朗日形式 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
+<li>LAR: 最小角回归 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
 <li>least squares: 最小二乘 (<a href="../03-Linear-Methods-for-Regression/3.2-Linear-Regression-Models-and-Least-Squares/index.html">第 3.2 节</a>)</li>
 <li>LDA: 线性判别分析 (<a href="../04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis/index.html">第 4.3 节</a>)</li>
 <li>low scale: 尺度最低 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>localized in time and in frequency: 在时间和在频率上局部化 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>linear smoother: 线性光滑 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
 <li>linear basis expansion: 线性基展开式 (<a href="../05-Basis-Expansions-and-Regularization/5.1-Introduction/index.html">第 5.1 节</a>)</li>
+<li>log-linear models: 对数线性模型 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
+<li>local linear logistic model: 局部线性逻辑斯蒂回归模型 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
 <li>local regression: 局部回归 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
 <li>lag set: 滞后集 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
-<li>local linear logistic model: 局部线性逻辑斯蒂回归模型 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
-<li>log-linear models: 对数线性模型 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
 <li>logistic: 逻辑斯蒂回归 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
 <li>light fitting: 轻拟合 (<a href="../07-Model-Assessment-and-Selection/7.11-Bootstrap-Methods/index.html">第 7.11 节</a>)</li>
 <li>LOO: 舍一法 (<a href="../07-Model-Assessment-and-Selection/7.12-Conditional-or-Expected-Test-Error/index.html">第 7.12 节</a>)</li>
@@ -3860,12 +3860,12 @@ <h2 id="l">L<a class="headerlink" href="#l" title="Permanent link">&para;</a></h
 <li>log-odds: 对数比率值 (<a href="../10-Boosting-and-Additive-Trees/10.5-Why-Exponential-Loss/index.html">第 10.5 节</a>)</li>
 <li>learning rate: 学习速度 (<a href="../11-Neural-Networks/11.4-Fitting-Neural-Networks/index.html">第 11.4 节</a>)</li>
 <li>log-likelihood loss: 对数似然损失 (<a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.3-Support-Vector-Machines-and-Kernels/index.html">第 12.3 节</a>)</li>
-<li>local multi-dimensional scaling, Local MDS: 局部多维缩放 (<a href="../14-Unsupervised-Learning/14.9-Nonlinear-Dimension-Reduction-and-Local-Multidimensional-Scaling/index.html">第 14.9 节</a>)</li>
 <li>Local MDS: 局部多维缩放 (<a href="../14-Unsupervised-Learning/14.9-Nonlinear-Dimension-Reduction-and-Local-Multidimensional-Scaling/index.html">第 14.9 节</a>)</li>
+<li>local multi-dimensional scaling, Local MDS: 局部多维缩放 (<a href="../14-Unsupervised-Learning/14.9-Nonlinear-Dimension-Reduction-and-Local-Multidimensional-Scaling/index.html">第 14.9 节</a>)</li>
 <li>Local linear embedding, LLE: 局部线性内嵌 (<a href="../14-Unsupervised-Learning/14.9-Nonlinear-Dimension-Reduction-and-Local-Multidimensional-Scaling/index.html">第 14.9 节</a>)</li>
 <li>linear manifolds: 线性流形 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
-<li>loadings: 载荷 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>, <a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>left singular vectors: 左奇异向量 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
+<li>loadings: 载荷 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>, <a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>least squares scaling: 最小二乘缩放 (<a href="../14-Unsupervised-Learning/14.8-Multidimensional-Scaling/index.html">第 14.8 节</a>)</li>
 <li>least angle regression: 最小角回归 (<a href="../16-Ensemble-Learning/16.2-Boosting-and-Regularization-Paths/index.html">第 16.2 节</a>)</li>
 <li>learning: 学习 (<a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>)</li>
@@ -3879,10 +3879,10 @@ <h2 id="m">M<a class="headerlink" href="#m" title="Permanent link">&para;</a></h
 <li>medium: 中 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>maximum likelihood estimation: 极大似然估计 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
 <li>model selection: 模型选择 (<a href="../03-Linear-Methods-for-Regression/3.3-Subset-Selection/index.html">第 3.3 节</a>)</li>
-<li>margin: 空白 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
 <li>margin: 边缘 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
-<li>marginal likelihood: 边缘似然 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
+<li>margin: 空白 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
 <li>multinomial: 多项式分布 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
+<li>marginal likelihood: 边缘似然 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>mother wavelet: 母波 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>moving beyond linearity: 超线性 (<a href="../05-Basis-Expansions-and-Regularization/5.1-Introduction/index.html">第 5.1 节</a>)</li>
 <li>memory-based: 基于内存的 (<a href="../06-Kernel-Smoothing-Methods/6.9-Computational-Consoderations/index.html">第 6.9 节</a>)</li>
@@ -3894,13 +3894,13 @@ <h2 id="m">M<a class="headerlink" href="#m" title="Permanent link">&para;</a></h
 <li>Markov chain Monte Carlo: 马尔科夫蒙特卡洛法 (<a href="../08-Model-Inference-and-Averaging/8.6-MCMC-for-Sampling-from-the-Posterior/index.html">第 8.6 节</a>)</li>
 <li>Multivariate Adaptive Regression Splines, MARS: 多变量自适应回归样条 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.4-MARS/index.html">第 9.4 节</a>)</li>
 <li>MARS: 多元自适应回归样条 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.0-Introduction/index.html">第 9.0 节</a>)</li>
-<li>MAR: 随机缺失 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.6-Missing-Data/index.html">第 9.6 节</a>)</li>
 <li>MCAR: 完全随机缺失 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.6-Missing-Data/index.html">第 9.6 节</a>)</li>
+<li>MAR: 随机缺失 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.6-Missing-Data/index.html">第 9.6 节</a>)</li>
 <li>median house values: 房子价值的中位数 (<a href="../10-Boosting-and-Additive-Trees/10.14-Illustrations/index.html">第 10.14 节</a>)</li>
 <li>multiple additive regression trees: 多重可加回归树 (<a href="../10-Boosting-and-Additive-Trees/10.10-Numerical-Optimization-via-Gradient-Boosting/index.html">第 10.10 节</a>)</li>
 <li>modal class: 众数 (<a href="../10-Boosting-and-Additive-Trees/10.9-Boosting-Trees/index.html">第 10.9 节</a>)</li>
-<li>multinomial deviance: 多项偏差 (<a href="../10-Boosting-and-Additive-Trees/10.6-Loss-Functions-and-Robustness/index.html">第 10.6 节</a>)</li>
 <li>margin: 间隔 (<a href="../10-Boosting-and-Additive-Trees/10.6-Loss-Functions-and-Robustness/index.html">第 10.6 节</a>)</li>
+<li>multinomial deviance: 多项偏差 (<a href="../10-Boosting-and-Additive-Trees/10.6-Loss-Functions-and-Robustness/index.html">第 10.6 节</a>)</li>
 <li>main effects: 主要影响 (<a href="../10-Boosting-and-Additive-Trees/10.8-Spam-Data/index.html">第 10.8 节</a>)</li>
 <li>meta-parameter: 元参数 (<a href="../10-Boosting-and-Additive-Trees/10.11-Right-Sized-Trees-for-Boosting/index.html">第 10.11 节</a>)</li>
 <li>marginal average: 边缘平均 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
@@ -3911,8 +3911,8 @@ <h2 id="m">M<a class="headerlink" href="#m" title="Permanent link">&para;</a></h
 <li>model-free: 不依赖具体模型的 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.1-Introduction/index.html">第 13.1 节</a>)</li>
 <li>memory-based: 基于存储的 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.3-k-Nearest-Neighbor-Classifiers/index.html">第 13.3 节</a>)</li>
 <li>melanomas: 黑素瘤 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
-<li>mode seeking: 模式寻找 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>mixture modeling: 混合模型 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
+<li>mode seeking: 模式寻找 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>missing values: 缺失值 (<a href="../14-Unsupervised-Learning/14.3-Cluster-Analysis/index.html">第 14.3 节</a>)</li>
 <li>manifold: 流形 (<a href="../14-Unsupervised-Learning/14.1-Introduction/index.html">第 14.1 节</a>)</li>
 <li>mode finding: 模式寻找 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
@@ -3920,16 +3920,16 @@ <h2 id="m">M<a class="headerlink" href="#m" title="Permanent link">&para;</a></h
 <li>mid-sagittal cross-section: 矢状面横截面 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>MDS, Multidimensional scaling: 多维缩放 (<a href="../14-Unsupervised-Learning/14.8-Multidimensional-Scaling/index.html">第 14.8 节</a>)</li>
 <li>metric: 度量 (<a href="../14-Unsupervised-Learning/14.8-Multidimensional-Scaling/index.html">第 14.8 节</a>)</li>
-<li>market confidence: 市场信心 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>mutual information: 互信息量 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
+<li>market confidence: 市场信心 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>memory: 记忆 (<a href="../16-Ensemble-Learning/16.3-Learning-Ensembles/index.html">第 16.3 节</a>)</li>
-<li>Markov random fields: 马尔科夫随机域 (<a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>)</li>
 <li>Markov networks: 马尔科夫网络 (<a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>)</li>
+<li>Markov random fields: 马尔科夫随机域 (<a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>)</li>
 <li>multiway tables of counts: 多路计数表 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 <li>maximal margin solution: 最大边缘解 (<a href="../18-High-Dimensional-Problems/18.5-Classification-When-Features-are-Unavailable/index.html">第 18.5 节</a>)</li>
 <li>multiple testing: 多重检验 (<a href="../18-High-Dimensional-Problems/18.7-Feature-Assessment-and-the-Multiple-Testing-Problem/index.html">第 18.7 节</a>, <a href="../18-High-Dimensional-Problems/18.1-When-p-is-Much-Bigger-than-N/index.html">第 18.1 节</a>)</li>
-<li>margin tree: 边际树 (<a href="../18-High-Dimensional-Problems/18.3-Linear-Classifiers-with-Quadratic-Regularization/index.html">第 18.3 节</a>)</li>
 <li>maximal margin classifier: 最大边界分类器 (<a href="../18-High-Dimensional-Problems/18.3-Linear-Classifiers-with-Quadratic-Regularization/index.html">第 18.3 节</a>)</li>
+<li>margin tree: 边际树 (<a href="../18-High-Dimensional-Problems/18.3-Linear-Classifiers-with-Quadratic-Regularization/index.html">第 18.3 节</a>)</li>
 </ul>
 <h2 id="n">N<a class="headerlink" href="#n" title="Permanent link">&para;</a></h2>
 <ul>
@@ -3948,11 +3948,11 @@ <h2 id="n">N<a class="headerlink" href="#n" title="Permanent link">&para;</a></h
 <li>negentropy: 负熵 (<a href="../14-Unsupervised-Learning/14.7-Independent-Component-Analysis-and-Exploratory-Projection-Pursuit/index.html">第 14.7 节</a>)</li>
 <li>neighborhood function: 邻居函数 (<a href="../14-Unsupervised-Learning/14.4-Self-Organizing-Maps/index.html">第 14.4 节</a>)</li>
 <li>numerical quadrature: 数值积分 (<a href="../16-Ensemble-Learning/16.3-Learning-Ensembles/index.html">第 16.3 节</a>)</li>
-<li>Newton updates: 牛顿更新 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 <li>nodes: 结点 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
-<li>nearest shrunken centroids, NSC: 最近收缩重心 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
+<li>Newton updates: 牛顿更新 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
 <li>nearest centroid classifer: 最近重心分类器 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
 <li>nearest centroid classifier: 最近重心分类器 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
+<li>nearest shrunken centroids, NSC: 最近收缩重心 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
 </ul>
 <h2 id="o">O<a class="headerlink" href="#o" title="Permanent link">&para;</a></h2>
 <ul>
@@ -3968,16 +3968,16 @@ <h2 id="o">O<a class="headerlink" href="#o" title="Permanent link">&para;</a></h
 <li>online training: 在线训练 (<a href="../11-Neural-Networks/11.4-Fitting-Neural-Networks/index.html">第 11.4 节</a>)</li>
 <li>Optimal scores: 最优得分 (<a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.5-Flexible-Disciminant-Analysis/index.html">第 12.5 节</a>)</li>
 <li>over-complete: 过充分 (<a href="../16-Ensemble-Learning/16.3-Learning-Ensembles/index.html">第 16.3 节</a>)</li>
-<li>ovo: 一对一方法 (<a href="../18-High-Dimensional-Problems/18.3-Linear-Classifiers-with-Quadratic-Regularization/index.html">第 18.3 节</a>)</li>
 <li>ova: 一对多方法 (<a href="../18-High-Dimensional-Problems/18.3-Linear-Classifiers-with-Quadratic-Regularization/index.html">第 18.3 节</a>)</li>
+<li>ovo: 一对一方法 (<a href="../18-High-Dimensional-Problems/18.3-Linear-Classifiers-with-Quadratic-Regularization/index.html">第 18.3 节</a>)</li>
 <li>overfitting: 过拟合 (<a href="../18-High-Dimensional-Problems/18.1-When-p-is-Much-Bigger-than-N/index.html">第 18.1 节</a>)</li>
 </ul>
 <h2 id="p">P<a class="headerlink" href="#p" title="Permanent link">&para;</a></h2>
 <ul>
 <li>PSA: 前列腺癌特定抗原水平 (<a href="../01-Introduction/1.1-Introduction/index.html">第 1.1 节</a>)</li>
 <li>predictors: 预测变量 (<a href="../02-Overview-of-Supervised-Learning/2.1-Introduction/index.html">第 2.1 节</a>)</li>
-<li>projection pursuit regression: 投影寻踪回归 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
 <li>prior belief: 先验信仰 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
+<li>projection pursuit regression: 投影寻踪回归 (<a href="../02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators/index.html">第 2.8 节</a>)</li>
 <li>prediction accuracy: 精确性 (<a href="../03-Linear-Methods-for-Regression/3.3-Subset-Selection/index.html">第 3.3 节</a>)</li>
 <li>principal components: 主成分 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
 <li>precomputer age of statistics: 前计算机时期 (<a href="../03-Linear-Methods-for-Regression/3.1-Introduction/index.html">第 3.1 节</a>)</li>
@@ -3988,8 +3988,8 @@ <h2 id="p">P<a class="headerlink" href="#p" title="Permanent link">&para;</a></h
 <li>parsimonious: 最简洁 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>pairwise variance: 逐点方差 (<a href="../05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines/index.html">第 5.2 节</a>)</li>
 <li>pyramidal schemes: 金字塔算法 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
-<li>penalty matrix: 惩罚矩阵 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
 <li>projection: 投影 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
+<li>penalty matrix: 惩罚矩阵 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
 <li>piecewise-polynimials: 分段多项式 (<a href="../05-Basis-Expansions-and-Regularization/5.1-Introduction/index.html">第 5.1 节</a>)</li>
 <li>prototype: 原型 (<a href="../06-Kernel-Smoothing-Methods/6.7-Radial-Basis-Functions-and-Kernels/index.html">第 6.7 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.7-Mixture-Discriminant-Analysis/index.html">第 12.7 节</a>)</li>
 <li>population linear regression: 总体线性回归 (<a href="../08-Model-Inference-and-Averaging/8.8-Model-Averaging-and-Stacking/index.html">第 8.8 节</a>)</li>
@@ -4004,18 +4004,18 @@ <h2 id="p">P<a class="headerlink" href="#p" title="Permanent link">&para;</a></h
 <li>predictive learning: 预测学习 (<a href="../10-Boosting-and-Additive-Trees/10.7-Off-the-Shelf-Procedures-for-Data-Mining/index.html">第 10.7 节</a>)</li>
 <li>partial dependence plot: 偏相依图 (<a href="../10-Boosting-and-Additive-Trees/10.8-Spam-Data/index.html">第 10.8 节</a>)</li>
 <li>primitive: 原型 (<a href="../10-Boosting-and-Additive-Trees/10.11-Right-Sized-Trees-for-Boosting/index.html">第 10.11 节</a>)</li>
-<li>purely multiplicative: 纯可乘的 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
-<li>purely additive: 纯可加的 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
 <li>partial dependence: 偏相依性 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
+<li>purely multiplicative: 纯可乘的 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
 <li>partial: 偏 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
+<li>purely additive: 纯可加的 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
 <li>PPR: 投影寻踪回归 (<a href="../11-Neural-Networks/11.2-Projection-Pursuit-Regression/index.html">第 11.2 节</a>)</li>
 <li>projection pursuit model: 投影寻踪模型 (<a href="../11-Neural-Networks/11.1-Introduction/index.html">第 11.1 节</a>)</li>
 <li>penalized discriminant analysis: 带惩罚的判别分析 (<a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.1-Introduction/index.html">第 12.1 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.4-Generalizing-Linear-Discriminant-Analysis/index.html">第 12.4 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.6-Penalized-Discriminant-Analysis/index.html">第 12.6 节</a>)</li>
 <li>prototype methods: 原型方法 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.2-Prototype-Methods/index.html">第 13.2 节</a>)</li>
 <li>pooled: 合并的 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.4-Adaptive-Nearest-Neighbor-Methods/index.html">第 13.4 节</a>)</li>
 <li>pattern classification: 模式识别 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.1-Introduction/index.html">第 13.1 节</a>)</li>
-<li>predictability: 可预测性 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
 <li>PRIM: 耐心规则诱导法 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
+<li>predictability: 可预测性 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
 <li>Procrustes: 普罗库鲁斯提斯 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>Principal points: 主点 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>projection matrix: 投影矩阵 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
@@ -4024,26 +4024,26 @@ <h2 id="p">P<a class="headerlink" href="#p" title="Permanent link">&para;</a></h
 <li>proximity plot: 邻近图 (<a href="../15-Random-Forests/15.3-Details-of-Random-Forests/index.html">第 15.3 节</a>)</li>
 <li>Percentage Misclassification Error Explained: 解释的误分类率的百分比 (<a href="../16-Ensemble-Learning/16.2-Boosting-and-Regularization-Paths/index.html">第 16.2 节</a>)</li>
 <li>Percentage Squared Prediction Explained: 解释的方差的百分比 (<a href="../16-Ensemble-Learning/16.2-Boosting-and-Regularization-Paths/index.html">第 16.2 节</a>)</li>
-<li>partition function: 分割函数 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
-<li>pairwise Markov graphs: 成对马尔科夫图 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 <li>pairwise Markov independencies: 逐对马尔科夫独立 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
-<li>pairwise Markov properties: 逐对马尔科夫性质 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
-<li>path: 路径 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 <li>potential function: 势函数 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 <li>partition: 分割 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
+<li>partition function: 分割函数 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
+<li>path: 路径 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
+<li>pairwise Markov graphs: 成对马尔科夫图 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
+<li>pairwise Markov properties: 逐对马尔科夫性质 (<a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>, <a href="../17-Undirected-Graphical-Models/17.2-Markov-Graphs-and-Their-Properties/index.html">第 17.2 节</a>)</li>
 <li>potential: 势 (<a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>, <a href="../17-Undirected-Graphical-Models/17.1-Introduction/index.html">第 17.1 节</a>)</li>
 <li>partial covariances: 偏协方差 (<a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>)</li>
 <li>Poisson log-linear modeling: 泊松对数线性建模 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
-<li>pooled within-class standard deviation: 合并类内标准差 (<a href="../18-High-Dimensional-Problems/18.5-Classification-When-Features-are-Unavailable/index.html">第 18.5 节</a>)</li>
 <li>proximity matrix: 相似性矩阵 (<a href="../18-High-Dimensional-Problems/18.5-Classification-When-Features-are-Unavailable/index.html">第 18.5 节</a>)</li>
+<li>pooled within-class standard deviation: 合并类内标准差 (<a href="../18-High-Dimensional-Problems/18.5-Classification-When-Features-are-Unavailable/index.html">第 18.5 节</a>)</li>
 <li>prior probability: 先验概率 (<a href="../18-High-Dimensional-Problems/18.2-Diagonal-Linear-Discriminant-Analysis-and-Nearest-Shrunken-Centroids/index.html">第 18.2 节</a>)</li>
 <li>permutation distribution: 置换分布 (<a href="../18-High-Dimensional-Problems/18.7-Feature-Assessment-and-the-Multiple-Testing-Problem/index.html">第 18.7 节</a>)</li>
 <li>posterior mode: 后验的众数 (<a href="../18-High-Dimensional-Problems/18.4-Linear-Classifiers-with-L1-Regularization/index.html">第 18.4 节</a>)</li>
 </ul>
 <h2 id="q">Q<a class="headerlink" href="#q" title="Permanent link">&para;</a></h2>
 <ul>
-<li>qualitative: 定性的 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>quantitative: 定量 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>, <a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
+<li>qualitative: 定性的 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>quality: 质量 (<a href="../07-Model-Assessment-and-Selection/7.1-Introduction/index.html">第 7.1 节</a>)</li>
 <li>query point: 查询点 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.4-Adaptive-Nearest-Neighbor-Methods/index.html">第 13.4 节</a>)</li>
 <li>query image: 查询图像 (<a href="../13-Prototype-Methods-and-Nearest-Neighbors/13.3-k-Nearest-Neighbor-Classifiers/index.html">第 13.3 节</a>)</li>
@@ -4064,9 +4064,9 @@ <h2 id="r">R<a class="headerlink" href="#r" title="Permanent link">&para;</a></h
 <li>residual deviance: 残偏差 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>regression splines: 回归样条 (<a href="../05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines/index.html">第 5.2 节</a>)</li>
 <li>reference: 参考 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
-<li>reproducing kernel Hilbert space: 再生核希尔伯特空间 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>RKHS: 再生核希尔伯特空间 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>reproducing kernel Hilbert spaces: 再生核希尔伯特空间 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.3-Support-Vector-Machines-and-Kernels/index.html">第 12.3 节</a>)</li>
+<li>reproducing kernel Hilbert space: 再生核希尔伯特空间 (<a href="../05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces/index.html">第 5.8 节</a>)</li>
 <li>radial basis functions: 径向基函数 (<a href="../05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines/index.html">第 5.7 节</a>)</li>
 <li>ratio scale: 定比型 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
 <li>relative overfitting rate: 相对过拟合率 (<a href="../07-Model-Assessment-and-Selection/7.11-Bootstrap-Methods/index.html">第 7.11 节</a>)</li>
@@ -4077,9 +4077,9 @@ <h2 id="r">R<a class="headerlink" href="#r" title="Permanent link">&para;</a></h
 <li>ridge function: 岭函数 (<a href="../11-Neural-Networks/11.2-Projection-Pursuit-Regression/index.html">第 11.2 节</a>)</li>
 <li>radical basis function network: 径向基函数网络 (<a href="../11-Neural-Networks/11.3-Neural-Networks/index.html">第 11.3 节</a>)</li>
 <li>regions: 区域 (<a href="../14-Unsupervised-Learning/14.2-Association-Rules/index.html">第 14.2 节</a>)</li>
+<li>rotation: 旋转 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>reconstruction error: 重构误差 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>right singular vectors: 右奇异向量 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
-<li>rotation: 旋转 (<a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
 <li>Random forests: 随机森林 (<a href="../15-Random-Forests/15.1-Introduction/index.html">第 15.1 节</a>)</li>
 <li>relevance network: 相关网络 (<a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>, <a href="../17-Undirected-Graphical-Models/17.3-Undirected-Graphical-Models-for-Continuous-Variables/index.html">第 17.3 节</a>)</li>
 <li>RBM: 限制玻尔兹曼机 (<a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>, <a href="../17-Undirected-Graphical-Models/17.4-Undirected-Graphical-Models-for-Discrete-Variables/index.html">第 17.4 节</a>)</li>
@@ -4090,8 +4090,8 @@ <h2 id="r">R<a class="headerlink" href="#r" title="Permanent link">&para;</a></h
 </ul>
 <h2 id="s">S<a class="headerlink" href="#s" title="Permanent link">&para;</a></h2>
 <ul>
-<li>simultaneously: 同时 (<a href="../02-Overview-of-Supervised-Learning/2.4-Statistical-Decision-Theory/index.html">第 2.4 节</a>)</li>
 <li>squared error loss: 平方误差损失 (<a href="../02-Overview-of-Supervised-Learning/2.4-Statistical-Decision-Theory/index.html">第 2.4 节</a>)</li>
+<li>simultaneously: 同时 (<a href="../02-Overview-of-Supervised-Learning/2.4-Statistical-Decision-Theory/index.html">第 2.4 节</a>)</li>
 <li>smoothing: 光滑 (<a href="../02-Overview-of-Supervised-Learning/2.9-Model-Selection-and-the-Bias-Variance-Tradeoff/index.html">第 2.9 节</a>)</li>
 <li>small: 小 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>supervised learning: 监督学习 (<a href="../02-Overview-of-Supervised-Learning/2.1-Introduction/index.html">第 2.1 节</a>, <a href="../14-Unsupervised-Learning/14.1-Introduction/index.html">第 14.1 节</a>)</li>
@@ -4100,37 +4100,37 @@ <h2 id="s">S<a class="headerlink" href="#s" title="Permanent link">&para;</a></h
 <li>smoothly clipped absolute deviation, SCAD: 平稳削减绝对偏差法 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
 <li>simple coordinate descent: 简单坐标下降 (<a href="../03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms/index.html">第 3.8 节</a>)</li>
 <li>subset selection: 子集选择 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
+<li>SVD: 奇异值分解 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
 <li>shrinkage methods: 收缩方法 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
 <li>stationary condition: 平稳条件 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
-<li>SVD: 奇异值分解 (<a href="../03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods/index.html">第 3.4 节</a>)</li>
-<li>slab: 平板 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
-<li>separating hyperplane classifiers: 分离超平面分类器 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
-<li>support points: 支撑点 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
-<li>support vector machine: 支持向量机 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.3-Support-Vector-Machines-and-Kernels/index.html">第 12.3 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.1-Introduction/index.html">第 12.1 节</a>)</li>
 <li>stochastic gradient descent: 随机梯度下降 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
+<li>support vector machine: 支持向量机 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.3-Support-Vector-Machines-and-Kernels/index.html">第 12.3 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.1-Introduction/index.html">第 12.1 节</a>)</li>
+<li>support points: 支撑点 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
+<li>separating hyperplane classifiers: 分离超平面分类器 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
+<li>slab: 平板 (<a href="../04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes/index.html">第 4.5 节</a>)</li>
 <li>self-consistency: 自一致性 (<a href="../04-Linear-Methods-for-Classification/4.4-Logistic-Regression/index.html">第 4.4 节</a>)</li>
 <li>sparse: 稀疏 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>, <a href="../16-Ensemble-Learning/16.2-Boosting-and-Regularization-Paths/index.html">第 16.2 节</a>)</li>
-<li>smoothing parameter: 光滑参数 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
 <li>smoother matrix: 光滑矩阵 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
 <li>shrinking: 收缩 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
+<li>smoothing parameter: 光滑参数 (<a href="../05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines/index.html">第 5.4 节</a>)</li>
 <li>splines: 样条 (<a href="../05-Basis-Expansions-and-Regularization/5.1-Introduction/index.html">第 5.1 节</a>)</li>
-<li>spurious: 假的 (<a href="../05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines/index.html">第 5.7 节</a>)</li>
 <li>stabilize: 稳定化 (<a href="../05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines/index.html">第 5.7 节</a>)</li>
+<li>spurious: 假的 (<a href="../05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines/index.html">第 5.7 节</a>)</li>
 <li>short-term history of the series: 序列的短期记忆 (<a href="../06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models/index.html">第 6.5 节</a>)</li>
 <li>state-of-the-art: 最先进的 (<a href="../06-Kernel-Smoothing-Methods/Bibliographic-Notes/index.html">第 6 章文献笔记</a>)</li>
 <li>scatter-cloud: 散点云 (<a href="../06-Kernel-Smoothing-Methods/6.3-Local-Regression-in-Rp/index.html">第 6.3 节</a>)</li>
 <li>span: 跨度 (<a href="../06-Kernel-Smoothing-Methods/6.2-Selecting-the-Width-of-the-Kernel/index.html">第 6.2 节</a>)</li>
-<li>sample standard deviation: 样本标准偏差 (<a href="../07-Model-Assessment-and-Selection/7.10-Cross-Validation/index.html">第 7.10 节</a>)</li>
 <li>standard error: 标准误差 (<a href="../07-Model-Assessment-and-Selection/7.10-Cross-Validation/index.html">第 7.10 节</a>)</li>
+<li>sample standard deviation: 样本标准偏差 (<a href="../07-Model-Assessment-and-Selection/7.10-Cross-Validation/index.html">第 7.10 节</a>)</li>
 <li>SRM, structural risk minimization: 结构风险最小化 (<a href="../07-Model-Assessment-and-Selection/7.9-Vapnik-Chervonenkis-Dimension/index.html">第 7.9 节</a>, <a href="../12-Support-Vector-Machines-and-Flexible-Discriminants/12.3-Support-Vector-Machines-and-Kernels/index.html">第 12.3 节</a>)</li>
-<li>stacking: 堆栈 (<a href="../08-Model-Inference-and-Averaging/8.8-Model-Averaging-and-Stacking/index.html">第 8.8 节</a>)</li>
 <li>Stacked generalization: 堆栈泛化 (<a href="../08-Model-Inference-and-Averaging/8.8-Model-Averaging-and-Stacking/index.html">第 8.8 节</a>)</li>
+<li>stacking: 堆栈 (<a href="../08-Model-Inference-and-Averaging/8.8-Model-Averaging-and-Stacking/index.html">第 8.8 节</a>)</li>
 <li>score function: 得分函数 (<a href="../08-Model-Inference-and-Averaging/8.2-The-Bootstrap-and-Maximum-Likelihood-Methods/index.html">第 8.2 节</a>)</li>
 <li>sensitivity: 敏感度 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods/index.html">第 9.2 节</a>)</li>
 <li>specificity: 特异度 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods/index.html">第 9.2 节</a>)</li>
 <li>surrogate splits: 代理分割 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.6-Missing-Data/index.html">第 9.6 节</a>)</li>
-<li>soft probabilistic: 软概率的决定 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
 <li>soft splits: 软分割 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
+<li>soft probabilistic: 软概率的决定 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.5-Hierarchical-Mixtures-of-Experts/index.html">第 9.5 节</a>)</li>
 <li>salinity: 盐度 (<a href="../10-Boosting-and-Additive-Trees/10.14-Illustrations/index.html">第 10.14 节</a>)</li>
 <li>step length: 步长 (<a href="../10-Boosting-and-Additive-Trees/10.10-Numerical-Optimization-via-Gradient-Boosting/index.html">第 10.10 节</a>)</li>
 <li>steepest descent: 最速下降 (<a href="../10-Boosting-and-Additive-Trees/10.10-Numerical-Optimization-via-Gradient-Boosting/index.html">第 10.10 节</a>)</li>
@@ -4158,25 +4158,25 @@ <h2 id="t">T<a class="headerlink" href="#t" title="Permanent link">&para;</a></h
 <li>training error: 训练误差 (<a href="../02-Overview-of-Supervised-Learning/2.9-Model-Selection-and-the-Bias-Variance-Tradeoff/index.html">第 2.9 节</a>, <a href="../07-Model-Assessment-and-Selection/7.4-Optimism-of-the-Training-Error-Rate/index.html">第 7.4 节</a>)</li>
 <li>training data: 训练数据 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
 <li>targets: 指标 (<a href="../02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology/index.html">第 2.2 节</a>)</li>
-<li>training: 训练集 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
 <li>teacher: 老师 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
+<li>training: 训练集 (<a href="../02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation/index.html">第 2.6 节</a>)</li>
 <li>training cases: 训练样本 (<a href="../03-Linear-Methods-for-Regression/3.1-Introduction/index.html">第 3.1 节</a>)</li>
 <li>target: 目标值 (<a href="../04-Linear-Methods-for-Classification/4.2-Linear-Regression-of-an-Indicator-Matrix/index.html">第 4.2 节</a>)</li>
 <li>trims down the hills and fills in the valleys: 坡顶被削减波谷被充填 (<a href="../05-Basis-Expansions-and-Regularization/5.5-Automatic-Selection-of-the-Smoothing-Parameters/index.html">第 5.5 节</a>)</li>
 <li>truncated-power: 截断幂 (<a href="../05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines/index.html">第 5.2 节</a>)</li>
-<li>time and frequency localization: 时间和频率的局部化 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>translation: 平移 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>, <a href="../14-Unsupervised-Learning/14.5-Principal-Components-Curves-and-Surfaces/index.html">第 14.5 节</a>)</li>
+<li>time and frequency localization: 时间和频率的局部化 (<a href="../05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing/index.html">第 5.9 节</a>)</li>
 <li>tensor product basis: 张量积基底 (<a href="../05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines/index.html">第 5.7 节</a>)</li>
 <li>triangulation schemes: 三角化方案 (<a href="../06-Kernel-Smoothing-Methods/6.9-Computational-Consoderations/index.html">第 6.9 节</a>)</li>
-<li>tie: 结 (<a href="../06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers/index.html">第 6.1 节</a>)</li>
 <li>trimming the hills: 截断山坡 (<a href="../06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers/index.html">第 6.1 节</a>)</li>
-<li>the bias-variance tradeoff: 偏差-方差之间的权衡 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
+<li>tie: 结 (<a href="../06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers/index.html">第 6.1 节</a>)</li>
 <li>test error: 测试误差 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
-<li>tunning parameter: 调整参数 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
 <li>Training error: 训练误差 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
+<li>the bias-variance tradeoff: 偏差-方差之间的权衡 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
+<li>tunning parameter: 调整参数 (<a href="../07-Model-Assessment-and-Selection/7.2-Bias-Variance-and-Model-Complexity/index.html">第 7.2 节</a>)</li>
 <li>training-set-size: 训练集大小偏差 (<a href="../07-Model-Assessment-and-Selection/7.11-Bootstrap-Methods/index.html">第 7.11 节</a>)</li>
-<li>The first surrogate: 第一代理 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods/index.html">第 9.2 节</a>)</li>
 <li>Tree-based: 基于树 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods/index.html">第 9.2 节</a>)</li>
+<li>The first surrogate: 第一代理 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.2-Tree-Based-Methods/index.html">第 9.2 节</a>)</li>
 <li>trees: 树 (<a href="../09-Additive-Models-Trees-and-Related-Methods/9.0-Introduction/index.html">第 9.0 节</a>)</li>
 <li>target function: 目标函数 (<a href="../10-Boosting-and-Additive-Trees/10.11-Right-Sized-Trees-for-Boosting/index.html">第 10.11 节</a>)</li>
 <li>trellis: 网格 (<a href="../10-Boosting-and-Additive-Trees/10.13-Interpretation/index.html">第 10.13 节</a>)</li>
@@ -4311,7 +4311,7 @@ <h2 id="__comments">Comments</h2>
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