A Geometric Nearest Point Algorithm for the Efficient Solution of the SVM Classification Task

Authors:
M. E. Mavroforakis;M. Sdralis;S. Theodoridis
Affiliations:
Univ. of Athens, Athens;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2007

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Abstract

Geometric methods are very intuitive and provide a theoretically solid approach to many optimization problems. One such optimization task is the support vector machine (SVM) classification, which has been the focus of intense theoretical as well as application-oriented research in machine learning. In this letter, the incorporation of recent results in reduced convex hulls (RCHs) to a nearest point algorithm (NPA) leads to an elegant and efficient solution to the SVM classification task, with encouraging practical results to real-world classification problems, i.e., linear or nonlinear and separable or nonseparable.