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 license and is available for download at https://github.com/LCSL/GURLS.
Cite
Text
Tacchetti et al. "GURLS: A Least Squares Library for Supervised Learning." Machine Learning Open Source Software, 2013.Markdown
[Tacchetti et al. "GURLS: A Least Squares Library for Supervised Learning." Machine Learning Open Source Software, 2013.](https://mlanthology.org/mloss/2013/tacchetti2013jmlr-gurls/)BibTeX
@article{tacchetti2013jmlr-gurls,
title = {{GURLS: A Least Squares Library for Supervised Learning}},
author = {Tacchetti, Andrea and Mallapragada, Pavan K. and Santoro, Matteo and Rosasco, Lorenzo},
journal = {Machine Learning Open Source Software},
year = {2013},
pages = {3201-3205},
volume = {14},
url = {https://mlanthology.org/mloss/2013/tacchetti2013jmlr-gurls/}
}