Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Kernel Adatron Algorithm for Supprot Vector Regression

  • Kyungha Seok (Dept. of Data Science, Inje University) ;
  • Changha Hwang (Dept. of Statistical Information, Catholic University)
  • Published : 1999.12.01
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Abstract

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.

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References

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