Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Support Vector Machine for Linear Regression

  • Published : 1999.08.01
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Abstract

Support vector machine(SVM) is a new and very promising regression and classification technique developed by Vapnik and his group at AT&T Bell laboratories. This article provides a brief overview of SVM focusing on linear regression. We explain from statistical point of view why SVM might be attractive and how this could be compared with other linear regression techniques. Furthermore. we explain model selection based on VC-theory.

Keywords

References

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