A Fast Bounded Parametric Margin Model for Support Vector Machine

In this paper, a fast bounded parametric margin  -support vector machine (BP- SVM) for classification is proposed. Different from the parametric margin  -support vector machine (par- -SVM), the BP- -SVM maximizes a bounded parametric margin, and consequently the successive overrelaxation (SOR) technique could be used to solve our dual problem as opposed solving the standard quadratic programming problem (QPP) in par- -SVM. Numerical experiments on several benchmark data sets and NDC data sets demonstrate the feasibility and effectiveness of the proposed algorithm.

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