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

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引用

Chapelle, O., & Schölkopf, B. (2002). Incorporating Invariances in Non-Linear Support Vector Machines. In T., Dietterich, S., Becker, & Z., Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 609-616). Cambridge, MA, USA: MIT Press.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-DF0B-7

要旨

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.