Efficient Feature Subset Selection for Support Vector Machines
Matthias Heiler, Daniel Cremers, Christoph Schnörr · 2001
Support vector machines can be regarded as algorithms for compressing information about class membership into a few support vectors with clear geometric interpretation. It is tempting to use this compressed information to select the most relevant input features. In this paper we present a method for doing so and provide evidence that it selects high-quality feature sets at a fraction of the costs of classical methods.