Piecewise-Smooth Support Vector Machine for Classification

Qing Wu and Wenqing Wang

Mathematical Problems in Engineering, 2013, vol. 2013, 1-7

Abstract:

Support vector machine (SVM) has been applied very successfully in a variety of classification systems. We attempt to solve the primal programming problems of SVM by converting them into smooth unconstrained minimization problems. In this paper, a new twice continuously differentiable piecewise-smooth function is proposed to approximate the plus function, and it issues a piecewise-smooth support vector machine (PWSSVM). The novel method can efficiently handle large-scale and high dimensional problems. The theoretical analysis demonstrates its advantages in efficiency and precision over other smooth functions. PWSSVM is solved using the fast Newton-Armijo algorithm. Experimental results are given to show the training speed and classification performance of our approach.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:135149

DOI: 10.1155/2013/135149

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