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GURLS: a Toolbox for Regularized Least Squares Learning

dc.date.accessioned2012-02-06T21:15:06Z
dc.date.accessioned2018-11-26T22:26:47Z
dc.date.available2012-02-06T21:15:06Z
dc.date.available2018-11-26T22:26:47Z
dc.date.issued2012-01-31
dc.identifier.urihttp://hdl.handle.net/1721.1/69034
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/69034
dc.description.abstractWe 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.en_US
dc.format.extent6 p.en_US
dc.publisherMIT CSAILen_US
dc.subjectMatlaben_US
dc.subjectComputational Learningen_US
dc.subjectRegularized Least Squaresen_US
dc.subjectLarge Scale, Multiclass problemsen_US
dc.subjectC++en_US
dc.titleGURLS: a Toolbox for Regularized Least Squares Learningen_US


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