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-------- Grand Unified Regularized Least Squares ---------
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Table of Contents
=================

- Introduction
- Documentation

Introduction
============

The GRAND UNIFIED REGULARIZED LEAST SQUARES software library comprises the following packages.

-GURLS, a MATLAB software library for regression and (multiclass) classification 
 based on the Regularized Least Squares (RLS) loss function. 
 Datasets that fit into your computer's memory should be handled with this package.

-bGURLS (b is for big), a MATLAB software library that allows to use RLS on very large
 matrices by means of memory-mapped storage and a simple distributed task manager.

-GURLS++, a C++ standalone implementation of GURLS, with additional simple API's for specific learning pipelines

-bGURLS++, a  C++ standalone implementation of bGURLS.

Documentation

=============

- GURLS webpage 
	http://lcsl.mit.edu/#/downloads/gurls

- Reference paper
	A. Tacchetti, P. K. Mallapragada, M. Santoro and L. Rosasco,
	GURLS: a Least Squares Library for Supervised Learning,
	Journal of Machine Learning Research, 14, 2013.
	http://jmlr.org/papers/v14/tacchetti13a.html 

- Installation instructions can be found here:
	https://github.com/LCSL/GURLS/wiki/2-Getting-Started

- Quick intructions on how to run the libraries for a default case:  
	https://github.com/LCSL/GURLS/wiki/2-Getting-Started#wiki-Hello_World

- A User manual with several examples:
	https://github.com/LCSL/GURLS/wiki/3-User-Manual#wiki-User_Manual

- A collection of the most useful and common pipelines: 
	https://github.com/LCSL/GURLS/wiki/3-User-Manual#wiki-Examples

- The list of all the available methods of the libraries: 
	https://github.com/LCSL/GURLS/wiki/4-Available-methods

- C++ Code Documentation: 
	http://lcsl.github.io/GURLS/

- Further Documentation
	* Have a look at the README files of each individual package.
	
	* In gurls-manual.pdf you can find both the installation instructions 
	  and user manual, together with the MATLAB and C++ Developer's Guide. 
	  GURLS is designed for easy expansion. Give it a try!
	
	* In recursiveRLS-tutorial.pdf you can find a simple Tutorial for the Recursive 
	  RLS API 

	* Description of the available methods, demos and data for each package: 
	 	https://github.com/CBCL/GURLS/wiki/4-Code-Description