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Feature Selection for SVMs

2001

Conference Paper

ei


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.

Author(s): Weston, J. and Mukherjee, S. and Chapelle, O. and Pontil, M. and Poggio, T. and Vapnik, V.
Book Title: Advances in Neural Information Processing Systems 13
Journal: Advances in Neural Information Processing Systems
Pages: 668-674
Year: 2001
Month: April
Day: 0
Editors: Leen, T.K. , T.G. Dietterich, V. Tresp
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000)
Event Place: Denver, CO, USA

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-12241-3
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{2164,
  title = {Feature Selection for SVMs},
  author = {Weston, J. and Mukherjee, S. and Chapelle, O. and Pontil, M. and Poggio, T. and Vapnik, V.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 13},
  pages = {668-674},
  editors = {Leen, T.K. , T.G. Dietterich, V. Tresp},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = apr,
  year = {2001},
  month_numeric = {4}
}