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  Feature Selection for SVMs

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E2AC-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E2AD-6
Genre: Conference Paper

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 Creators:
Weston, J1, Author           
Mukherjee S, Chapelle, O1, Author           
Pontil M, Poggio, T, Author
Vapnik, V, Author
Leen, Editor
T.K., Editor
Dietterich, T.G., Editor
Tresp, V., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Dates: 2001-04
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-12241-3
URI: http://books.nips.cc/nips13.html
BibTex Citekey: 2164
 Degree: -

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Title: Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000)
Place of Event: Denver, CO, USA
Start-/End Date: -

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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 668 - 674 Identifier: -