From 7a5d4b4629fc0c81e711b77f896137caf15621c4 Mon Sep 17 00:00:00 2001
From: Rohit Sivaprasad <rohit.sprasad@gmail.com>
Date: Sat, 21 Sep 2013 02:35:05 -0400
Subject: [PATCH] DOC typo in SVM narrative

Fixes #2465.
---
 doc/modules/svm.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst
index 0054fdf61e368..eec8bf019bfe3 100644
--- a/doc/modules/svm.rst
+++ b/doc/modules/svm.rst
@@ -34,7 +34,7 @@ The disadvantages of support vector machines include:
       calculated using an expensive five-fold cross-validation
       (see :ref:`Scores and probabilities <scores_probabilities>`, below).
 
-The support vector machines in scikit-learn support both dens
+The support vector machines in scikit-learn support both dense
 (``numpy.ndarray`` and convertible to that by ``numpy.asarray``) and
 sparse (any ``scipy.sparse``) sample vectors as input. However, to use
 an SVM to make predictions for sparse data, it must have been fit on such