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The Support Vector method

  • Part II: Cortical Maps and Receptive Fields
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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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.

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References

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Author information

Authors and Affiliations

  1. AT&T Labs-Research, 100 Schultz Dr. Red Bank, 07701, NJ, USA

    Vladimir N. Vapnik

Authors
  1. Vladimir N. Vapnik
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Vapnik, V.N. (1997). The Support Vector method. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020166

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  • DOI: https://doi.org/10.1007/BFb0020166

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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