Feature Selection for SVMs
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.
Cite
Text
Weston et al. "Feature Selection for SVMs." Neural Information Processing Systems, 2000.Markdown
[Weston et al. "Feature Selection for SVMs." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/weston2000neurips-feature/)BibTeX
@inproceedings{weston2000neurips-feature,
title = {{Feature Selection for SVMs}},
author = {Weston, Jason and Mukherjee, Sayan and Chapelle, Olivier and Pontil, Massimiliano and Poggio, Tomaso and Vapnik, Vladimir},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {668-674},
url = {https://mlanthology.org/neurips/2000/weston2000neurips-feature/}
}