Support Vector Machine and Generalization
Neurosceince Research Institute, National Institute of Advanced Indastrial Science and Technology, Umezono 1-1-1, Tsukuba, Ibaraki 305-8568, Japan
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.
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