Support Vector Machine and Generalization

Takio Kurita

doi:10.20965/jaciii.2004.p0084

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JACIII Vol.8 No.2 pp. 84-92

doi: 10.20965/jaciii.2004.p0084

(2004)

Paper:

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Support Vector Machine and Generalization

Takio Kurita

Neurosceince Research Institute, National Institute of Advanced Indastrial Science and Technology, Umezono 1-1-1, Tsukuba, Ibaraki 305-8568, Japan

Received:

August 11, 2003

Accepted:

December 1, 2003

Published:

March 20, 2004

Keywords:

Perceptron, support vector machine, logistic regression, generalization, shrinkage method, ridge regression, feature selection, model selection, weight decay

Abstract

The support vector machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data. This paper reviews how to enhance generalization in learning classifiers centering on the SVM.

Cite this article as:

T. Kurita, “Support Vector Machine and Generalization,” J. Adv. Comput. Intell. Intell. Inform., Vol.8, No.2, pp. 84-92, 2004.

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