Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Bayesian Model Selection for Support Vector Regression using the Evidence Framework

  • Hwang, Chang-Ha (Dept. of Statistical Information, Catholic University of Taegu-Hyosung) ;
  • Seok, Kyung-Ha (Dept. of Data Science Inje University)
  • Published : 1999.12.01
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Abstract

Supprot vector machine(SVM) is a new and very promising regression and classification technique developed by Vapnik and his group at AT&T Bell Laboratories. in this paper we provide a brief overview of SVM for regression. Furthermore we describe Bayesian model selection based on macKay's evidence framework for SVM regression.

Keywords

References

  1. Neural Networks for Pattern Recognition Bishop, C.M.
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  6. Neuro COLT2 Technical Report A Tutorial on Support Vector Regression Smola, A.J.;Scholkopf, B.
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  8. Statistical Learning Theory Vapnik, V.