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  Incorporating Invariances in Non-Linear Support Vector Machines

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.

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 Creators:
Chapelle, O, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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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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 Dates: 2002-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 1820
 Degree: -

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Title: Fifteenth Annual Neural Information Processing Systems Conference (NIPS 2001)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2001-12-03 - 2001-12-08

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Title: Advances in Neural Information Processing Systems 14
Source Genre: Proceedings
 Creator(s):
Dietterich, TG, Editor
Becker, S, Editor
Ghahramani, Z, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 609 - 616 Identifier: ISBN: 0-262-27173-7