Piecewise-Smooth Support Vector Machine for Classification

Piecewise-Smooth Support Vector Machine for Classification

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-smoo...

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Main Authors: Qing Wu, Wenqing Wang
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2013/135149
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spelling doaj-4fb045993c494551b832751ef0d1a3bd2020-11-24T22:15:21ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/135149135149Piecewise-Smooth Support Vector Machine for ClassificationQing Wu0Wenqing Wang1School of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, ChinaSchool of Automation, Xi'an University of Posts and Telecommunications, Xi'an 710121, ChinaSupport 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.http://dx.doi.org/10.1155/2013/135149
collection DOAJ
language English
format Article
sources DOAJ
author Qing Wu
Wenqing Wang
spellingShingle Qing Wu
Wenqing Wang
Piecewise-Smooth Support Vector Machine for Classification
Mathematical Problems in Engineering
author_facet Qing Wu
Wenqing Wang
author_sort Qing Wu
title Piecewise-Smooth Support Vector Machine for Classification
title_short Piecewise-Smooth Support Vector Machine for Classification
title_full Piecewise-Smooth Support Vector Machine for Classification
title_fullStr Piecewise-Smooth Support Vector Machine for Classification
title_full_unstemmed Piecewise-Smooth Support Vector Machine for Classification
title_sort piecewise-smooth support vector machine for classification
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2013-01-01
description 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.
url http://dx.doi.org/10.1155/2013/135149
work_keys_str_mv AT qingwu piecewisesmoothsupportvectormachineforclassification
AT wenqingwang piecewisesmoothsupportvectormachineforclassification
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