Incorporating Invariances in Non-Linear Support Vector Machines

Chapelle, O; Schölkopf, B

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Incorporating Invariances in Non-Linear Support Vector Machines

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Chapelle, O., & Schölkopf, B.(2001). Incorporating Invariances in Non-Linear Support Vector Machines.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E3A0-3

Abstract

We consider the problem of how to incorporate in the Support Vector Machine (SVM ) framework invariances given by some a priori known transformations under which t he data should be invariant. It extends some previous work which was only applicab le with linear SVMs and we show on a digit recognition task that the proposed appro ach is superior to the traditional Virtual Support Vector method.