Feature Selection for SVMs
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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 micro array data.
Date issued
2000Department
Massachusetts Institute of Technology. Center for Biological & Computational Learning; Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
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