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| A Sparse Least Squares Support Vector Machine Classifier |
| LIU XiaoMao1, KONG Bo1, GAO JunBin2, ZHANG Jun3 |
1.Department of Mathematics, Huazhong University of Science and Technology, Wuhan 430074
2.School of Information Technology, Charles Sturt University, Bathurst, NSW 2795, Australia
3.State Key Laboratory for MultiSpectral Information Processing Technologies, Huazhong University of Science and Technology, Wuhan 430074 |
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Abstract Support Vector Machine (SVM) has to solve the quadratic programming problem, while least squares support vector machine (LSSVM) only needs to deal with the linear equations. However the defect of LSSVM is the lack of sparseness. In this paper, a method named sparse least squares support vector machine classifier (SLSSVM) is presented to remedy the defect of the LSSVM. It is carried out by preextracting margin vectors using center distance ratio method as original training samples and putting those which have not been classified correctly in the first training together as new training samples. The proposed method not only remedies the defect of LSSVM, but also speeds up training and classifying. Furthermore, it can rectify the deviation of the classifier for unbalanced training data and the classifying ability is not affected. The good performance of SLSSVM is verified on several data sets.
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Received: 29 March 2006
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2007, Vol. 20 
