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83259 |
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|a Tacchetti, Andrea
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|a Massachusetts Institute of Technology. Center for Biological & Computational Learning
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a McGovern Institute for Brain Research at MIT
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|a Tacchetti, Andrea
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|a Mallapragada, Pavan K.
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|a Mallapragada, Pavan K.
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|a Santoro, Matteo
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|a Rosasco, Lorenzo
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|a GURLS: A Least Squares Library for Supervised Learning
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|b Association for Computing Machinery (ACM),
|c 2013-12-23T21:27:44Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/83259
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|a 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.
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|a en_US
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|a Article
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|t Journal of Machine Learning Research
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