[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CSAM.2015.22.2.201

Kernel-Trick Regression and Classification

Huh, Myung-Hoe (Department of Statistics, Korea University)

Publication Information

Communications for Statistical Applications and Methods / v.22, no.2, 2015 , pp. 201-207 More about this Journal

Abstract

Support vector machine (SVM) is a well known kernel-trick supervised learning tool. This study proposes a working scheme for kernel-trick regression and classification (KtRC) as a SVM alternative. KtRC fits the model on a number of random subsamples and selects the best model. Empirical examples and a simulation study indicate that KtRC's performance is comparable to SVM.

Keywords

Kernel trick; support vector machine; subsampling; cross-validation;

Citations & Related Records

Reference

1	Fukumizu, K. (2010). Introduction to Kernel Methods, (written in Japanese), Asakura Publishing, 8-9.
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3	Scholkopf, B. and Smola, A. (1998). Learning with Kernels, MIT Press, 118-120.