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 f...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery (ACM)
2013
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| Online Access: | http://hdl.handle.net/1721.1/83259 https://orcid.org/0000-0001-9311-9171 |
| _version_ | 1826214278558908416 |
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| author | Tacchetti, Andrea Mallapragada, Pavan K. Santoro, Matteo Rosasco, Lorenzo |
| author2 | Massachusetts Institute of Technology. Center for Biological & Computational Learning |
| author_facet | Massachusetts Institute of Technology. Center for Biological & Computational Learning Tacchetti, Andrea Mallapragada, Pavan K. Santoro, Matteo Rosasco, Lorenzo |
| author_sort | Tacchetti, Andrea |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:40:43Z |
| format | Article |
| id | mit-1721.1/83259 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:40:43Z |
| publishDate | 2013 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/832592022-09-27T14:10:45Z GURLS: A Least Squares Library for Supervised Learning Tacchetti, Andrea Mallapragada, Pavan K. Santoro, Matteo Rosasco, Lorenzo 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 Tacchetti, Andrea Mallapragada, Pavan K. 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. 2013-12-23T21:27:44Z 2013-12-23T21:27:44Z 2013-10 2013-02 Article http://purl.org/eprint/type/JournalArticle 1532-4435 1533-7928 http://hdl.handle.net/1721.1/83259 Tacchetti, Andrea, et al. "Gurls: A Least Squares Library for Supervised Learning." Journal of Machine Learning Research 14 (2013): 3201-05. https://orcid.org/0000-0001-9311-9171 en_US http://jmlr.org/papers/v14/tacchetti13a.html Journal of Machine Learning Research 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. application/pdf Association for Computing Machinery (ACM) Journal of Machine Learning Research |
| spellingShingle | Tacchetti, Andrea Mallapragada, Pavan K. Santoro, Matteo Rosasco, Lorenzo GURLS: A Least Squares Library for Supervised Learning |
| title | GURLS: A Least Squares Library for Supervised Learning |
| title_full | GURLS: A Least Squares Library for Supervised Learning |
| title_fullStr | GURLS: A Least Squares Library for Supervised Learning |
| title_full_unstemmed | GURLS: A Least Squares Library for Supervised Learning |
| title_short | GURLS: A Least Squares Library for Supervised Learning |
| title_sort | gurls a least squares library for supervised learning |
| url | http://hdl.handle.net/1721.1/83259 https://orcid.org/0000-0001-9311-9171 |
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