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NIPS
2000

Feature Selection for SVMs

13 years 5 months ago
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 feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.
Jason Weston, Sayan Mukherjee, Olivier Chapelle, M
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where NIPS
Authors Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik
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