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
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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