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Conference Paper

Feature Selection for SVMs

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84311

Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83855

Mukherjee S, Chapelle,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Weston, J., Mukherjee S, Chapelle, O., Pontil M, Poggio, T., & Vapnik, V. (2001). Feature Selection for SVMs. Advances in Neural Information Processing Systems, 668-674.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E2AC-8
Abstract
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 feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.