GURLS: a Toolbox for Regularized Least Squares Learning
Author(s)Tacchetti, Andrea; Mallapragada, Pavan S.; Santoro, Matteo; Rosasco, Lorenzo
Center for Biological and Computational Learning (CBCL)
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We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm. The toolbox takes advantage of all the favorable properties of least squares and is tailored to deal in particular with multi-category/multi-label problems. One of the main advantages of GURLS is that it allows training and tuning a multi-category classifier at essentially the same cost of one single binary classifier. The toolbox provides a set of basic functionalities including different training strategies and routines to handle computations with very large matrices by means of both memory-mapped storage and distributed task execution. The system is modular and can serve as a basis for easily prototyping new algorithms. The toolbox is available for download, easy to set-up and use.
Matlab, Computational Learning, Regularized Least Squares, Large Scale, Multiclass problems, C++