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The Support Vector method

  • Vladimir N. Vapnik
Part II: Cortical Maps and Receptive Fields
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

The Support Vector (SV) method is a new general method of function estimation which does not depend explicitly on the dimensionality of input space. It was applied for pattern recognition, regression estimation, and density estimation problems as well as for problems of solving linear operator equations. In this article we describe the general idea of the SV method and present theorems demonstrating that the generalization ability of the SV method is based on factors which classical statistics do not take into account. We also describe the SV method for density estimation in a set of functions defined by a mixture of an infinite number of Gaussians.

Keywords

Support Vector Generalization Ability Pattern Recognition Problem Optimal Hyperplane Kernel Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifier. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. pp 144–152, Pittsburgh, PA, 1992.Google Scholar
  2. 2.
    C. Cortes and V.Vapnik. Support Vector Network. Machine Learning. 20:273–297, 1995.Google Scholar
  3. 3.
    H. Drucker, C.J. Burges, L. Kaufman, A. Smola, and V. Vapnik. Support vector regression machines. In Advances in Neural Information Processing Systems 9, 1997, MIT Press.Google Scholar
  4. 4.
    V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995, New-York.Google Scholar
  5. 5.
    V.N. Vapnik and A. Ya. Chervonenkis. Theory of Pattern Recognition (in Russian) Nauka, Moscow, 1974. German translation: W.N. Wapnik, A Ja. Tscherwonenkis Teorie der Zeichenerkennung, Akademia, Berlin, 1979.Google Scholar
  6. 6.
    V. Vapnik, S. Golowich, and A. Smola. Support vector method for function approximation, regression estimation and signal processing. In Advances in Neural Information Processing Systems, 9., 1997 MIT Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Vladimir N. Vapnik
    • 1
  1. 1.AT&T Labs-ResearchUSA

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