piecewise-smooth support vector machine for classification

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ID: 152774
2013
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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.
Reference Key
wu2013mathematicalpiecewise-smooth Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Qing Wu;Wenqing Wang
Journal journal of power sources
Year 2013
DOI
10.1155/2013/135149
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