Machine Learning

, Volume 46, Issue 1, pp 161–190

Training Invariant Support Vector Machines

  • Dennis Decoste
  • Bernhard Schölkopf
Article

DOI: 10.1023/A:1012454411458

Cite this article as:
Decoste, D. & Schölkopf, B. Machine Learning (2002) 46: 161. doi:10.1023/A:1012454411458

Abstract

Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the lowest reported test error on the well-known MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than previous SVM methods.

support vector machinesinvarianceprior knowledgeimage classificationpattern recognition
Download to read the full article text

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Dennis Decoste
    • 1
    • 2
  • Bernhard Schölkopf
    • 3
  1. 1.Jet Propulsion Laboratory, MS 126-347PasadenaUSA;
  2. 2.California Institute of TechnologyUSA
  3. 3.Max-Planck-Institut fuer biologische KybernetikTübingenGermany