Fast training of support vector machines using top-down kernel clustering

Abstract

How to deal with the very large database in decision-making applications is a very important issue, which sometimes can be addressed using SVMs. This paper presents a new sample reduction algorithm as a sampling preprocessing for SVM training to improve the scalability. We develop a novel top-down kernel clustering approach which tends to fast produce… (More)

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