An improvement on parametric \(\nu \)-support vector algorithm for classification
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One effective technique that has recently been considered for solving classification problems is parametric \(\nu \)-support vector regression. This method obtains a concurrent learning framework for both margin determination and function approximation and leads to a convex quadratic programming problem. In this paper we introduce a new idea that converts this problem into an unconstrained convex problem. Moreover, we propose an extension of Newton’s method for solving the unconstrained convex problem. We compare the accuracy and efficiency of our method with support vector machines and parametric \(\nu \)-support vector regression methods. Experimental results on several UCI benchmark data sets indicate the high efficiency and accuracy of this method.
KeywordsClassification Support vector regression \(\nu \)-support vector machines Parametric \(\nu \)-support vector Generalized Newton method Parametric margin
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