Feature Selection for SVMs
2001
Conference Paper
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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): | Empirische Inferenz |
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: |
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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}, doi = {}, month_numeric = {4} } |