piecewise-smooth support vector machine for classification
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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
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|---|---|
| Authors | ;Qing Wu;Wenqing Wang |
| Journal | journal of power sources |
| Year | 2013 |
| DOI |
10.1155/2013/135149
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| URL | |
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