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Incorporating invariances in support vector learning machines

  • Bernhard Schölkopf
  • Chris Burges
  • Vladimir Vapnik
Oral Presentations: Theory Theory II: Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)

Abstract

Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Bernhard Schölkopf
    • 1
    • 2
  • Chris Burges
    • 2
  • Vladimir Vapnik
    • 2
  1. 1.Max-Planck-Institut für biologische KybernetikTübingenGermany
  2. 2.AT&T Bell LaboratoriesHolmdelUSA

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