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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.
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