ICANN 98 pp 111-116 | Cite as

Support Vector Regression with Automatic Accuracy Control

  • B. Schölkopf
  • P. Bartlett
  • A. Smola
  • R. Williamson
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

A new algorithm for Support Vector regression is proposed. For a priori chosen ν, it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction ν of the data points lie outside. The algorithm is analysed theoretically and experimentally.

Keywords

Eter 

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References

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    B. Schölkopf, C. Burges, and V. Vapnik. Extracting support data for a given task. In U. M. Fayyad and R. Uthurusamy, editors, Proceedings, First International Conference on Knowledge Discovery & Data Mining. AAAI Press, Menlo Park, CA, 1995.Google Scholar
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    A. Smola, N. Murata, B. Schölkopf, and K.-R. Müller. Asymptotically optimal choice of ε-loss for support vector machines. ICANN’98.Google Scholar
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    V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • B. Schölkopf
    • 1
    • 2
  • P. Bartlett
    • 1
  • A. Smola
    • 1
    • 2
  • R. Williamson
    • 1
  1. 1.FEIT/RSISEAustralian National UniversityCanberraAustralia
  2. 2.GMD FIRSTBerlinGermany

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