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

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3 Author(s)
Mavroforakis, M.E. ; Univ. of Athens, Athens ; Sdralis, M. ; Theodoridis, S.

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.

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Neural Networks, IEEE Transactions on  (Volume:18 ,  Issue: 5 )