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Piecewise-Smooth Support Vector Machine for Classification

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  • Qing Wu
  • Wenqing Wang

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.

Suggested Citation

  • Qing Wu & Wenqing Wang, 2013. "Piecewise-Smooth Support Vector Machine for Classification," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, April.
    • Handle: RePEc:hin:jnlmpe:135149
    DOI: 10.1155/2013/135149
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