1.华中科技大学 数学系 武汉 430074 2.School of Information Technology,Charles Sturt University, Bathurst, NSW 2795, Australia 3.华中科技大学 多谱信息处理技术重点实验室 武汉 430074
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
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.
刘小茂，孔波，高俊斌，张钧. 一种稀疏最小二乘支持向量分类机*[J]. 模式识别与人工智能, 2007, 20(5): 681-687.
LIU XiaoMao , KONG Bo , GAO JunBin , ZHANG Jun. A Sparse Least Squares Support Vector Machine Classifier. , 2007, 20(5): 681-687.
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