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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. In T. Leen, T. Dietterich, & V. Tresp (Eds.), Advances in Neural Information Processing Systems 13 (pp. 668-674). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E2AC-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-97F5-B
Genre: Conference Paper

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 Creators:
Weston, J1, Author              
Mukherjee, S, Author
Chapelle, O1, Author              
Pontil , M, Author
Poggio, T, Author              
Vapnik, V, Author              
Affiliations:
1External Organizations, ou_persistent22              

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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: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: 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: 2000-11-27 - 2000-12-02

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Title: Advances in Neural Information Processing Systems 13
Source Genre: Proceedings
 Creator(s):
Leen, TK, Editor
Dietterich, TG, Editor
Tresp, V, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 668 - 674 Identifier: ISBN: 0-262-12241-3