Feature Selection for SVMs
Name
Poggio_feature-selection-for-svms.pdf
Size
1.25 MB
Format
Adobe PDF
Checksum (MD5)
112864b41a0519d8c57ebfa0e867ffaa
Author(s)
Poggio, Tomaso A.
Weston, Jason
Mukherjee, Sayan
Pontil, Massimiliano
Chapelle, Olivier
Vapnik, Vladimir
Date Issued
2000
Journal
Advances in Neural Information Processing Systems (NIPS)
Publisher
Neural Information Processing Systems Foundation
Citation
Weston, J., S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. "Feature Selection for SVMs." Advances in Neural Information Processing Systems 13 (NIPS 2000). © 2000 Neural Information Processing Systems Foundation, Inc.
Version
Final published version
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 micro array data.
MIT Department
Massachusetts Institute of Technology. Center for Biological & Computational Learning
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/1850-feature-selection-for-svms
