Incorporating invariances in nonlinear Support Vector Machines
Olivier Chapelle, Bernhard Schölkopf · MPG.PuRe (Max Planck Society) · 2001
The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a digit recognition task that the proposed approach is superior to the Virtual Support Vector method, which previously had been the method of choice.