A modified algorithm for nonconvex support vector classification

Authors:
Akiko Takeda
Affiliations:
Tokyo Institute of Technology, Oh-Okayama, Meguro-ku, Tokyo, Japan
Venue:
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Year:
2008

Citing 4
Cited 1

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Abstract

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 Extended 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 modification 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.