GURLS: A Least Squares Library for Supervised Learning
Author(s)Tacchetti, Andrea; Mallapragada, Pavan K.; Santoro, Matteo; Rosasco, Lorenzo
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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.
DepartmentMassachusetts 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
Journal of Machine Learning Research
Association for Computing Machinery (ACM)
Tacchetti, Andrea, et al. "Gurls: A Least Squares Library for Supervised Learning." Journal of Machine Learning Research 14 (2013): 3201-05.
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