GURLS: a least squares library for supervised learning

  • Authors:

  • Andrea Tacchetti;Pavan K. Mallapragada;Matteo Santoro;Lorenzo Rosasco

  • Affiliations:

  • Center for Biological and Computational Learning, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA and Laboratory for Computational and Statistical Learn ...;Center for Biological and Computational Learning, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA;Laboratory for Computational and Statistical Learning, Istituto Italiano di Tecnologia, Genova, Italy;DIBRIS, Università degli Studi di Genova, Genova, Italy and Laboratory for Computational and Statistical Learning, Istituto Italiano di Tecnologia and Massachusetts Institute of Technology

  • Venue:
  • The Journal of Machine Learning Research
  • Year:
  • 2013

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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.