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Incorporating Invariances in Non-Linear Support Vector Machines

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83855

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

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

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Zitation

Chapelle, O., & Schölkopf, B. (2002). Incorporating Invariances in Non-Linear Support Vector Machines. Advances in Neural Information Processing Systems, 609-616.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DF0B-7
Zusammenfassung
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.