GURLS: a Least Squares Library for Supervised Learning
Abstract
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.
Cite
@article{arxiv.1303.0934,
title = {GURLS: a Least Squares Library for Supervised Learning},
author = {Andrea Tacchetti and Pavan K Mallapragada and Matteo Santoro and Lorenzo Rosasco},
journal= {arXiv preprint arXiv:1303.0934},
year = {2013}
}