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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, 2003Accepted:December 1, 2003Published: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.Data files:
