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Abstract:
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.
