GURLS: a Least Squares Library for Supervised Learning

We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non-specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines fo...

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Bibliographic Details
Published inarXiv.org
Main Authors Tacchetti, Andrea, Mallapragada, Pavan K, Santoro, Matteo, Rosasco, Lorenzo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.03.2013
Subjects
Algorithms
Distributed memory
Downloading
Least squares
Libraries
Linear algebra
Machine learning
Mathematical analysis
Matrix methods
Routines
Supervised learning
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Summary:We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non-specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to exploit advanced tools in linear algebra. Routines to handle computations with very large matrices by means of memory-mapped storage and distributed task execution are available. The package is distributed under the BSD licence and is available for download at https://github.com/CBCL/GURLS.
ISSN:2331-8422