A. Takeda (Japan)
support vector machines, conditional value-at-risk, non convex quadratic programming, extended ν-SVM.
Support vector classifications (SVCs) are widely used as computationally powerful tools for binary classification. As an extension of ν-SVC, Perez-Cruz et al. proposed Ex tended ν-SVC where a nonconvex quadratic programming (QP) problem is formulated and an iterative algorithm is applied to the problem. In the paper, we propose a mod ification for the existing algorithm of Extended ν-SVC, which makes possible to analyze the finite convergence and local optimality of the algorithm. The modification is done so that the algorithm visits only a finite number of basic solutions of the nonconvex QP problem. Though the modification is theoretically rather than practically important, experimental results also show that the modification causes the algorithm to finish faster.
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