GURLS: A Least Squares Library for Supervised Learning
Name
Tacchetti-2013-Gurls a least.pdf
Size
73.48 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Tacchetti, Andrea
Mallapragada, Pavan K.
Santoro, Matteo
Rosasco, Lorenzo
Date Issued
October 2013
Journal
Journal of Machine Learning Research
Publisher
Association for Computing Machinery (ACM)
Citation
Tacchetti, Andrea, et al. "Gurls: A Least Squares Library for Supervised Learning." Journal of Machine Learning Research 14 (2013): 3201-05.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Center for Biological & Computational Learning
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
McGovern Institute for Brain Research at MIT
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://jmlr.org/papers/v14/tacchetti13a.html
