Feature Selection for SVMs

Feature Selection for SVMs

We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard...

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Bibliographic Details
Main Authors: Poggio, Tomaso A. (Contributor), Weston, Jason (Author), Mukherjee, Sayan (Contributor), Pontil, Massimiliano (Contributor), Chapelle, Olivier (Author), Vapnik, Vladimir (Author)
Other Authors: Massachusetts Institute of Technology. Center for Biological & Computational Learning (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
Format: Article
Language:English
Published: Neural Information Processing Systems Foundation, 2016-05-13T18:41:58Z.
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