| Support vector classification algorithm based on variable parameter linear programming |
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| 作者单位： | Xiao Jianhua（Systems Science and Technology Inst., Wuyi Univ., Jiangmen 529020, P. R. China;School of Economy and Management, Beijing Univ. of Aeronautics and Astronautics, Beijing 100083, P. R. China） ;
Lin Jian（Systems Science and Technology Inst., Wuyi Univ., Jiangmen 529020, P. R. China） ; |
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| 基金项目： | 国家自然科学基金;中国博士后科学基金;广东省科技厅科技计划 |
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| 摘 要： | To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed. In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model. The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given. An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.
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| 收稿时间： | 24 February 2006 |
Support vector classification algorithm based on variable parameter linear programming |
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| Authors: | Xiao Jianhua Lin Jian |
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| Affiliation: | 1. Systems Science and Technology Inst., Wuyi Univ., Jiangmen 529020, P. R. China;School of Economy and Management, Beijing Univ. of Aeronautics and Astronautics, Beijing 100083, P. R. China
2. Systems Science and Technology Inst., Wuyi Univ., Jiangmen 529020, P. R. China |
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| Abstract: | To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed. In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model. The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given. An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples. |
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| Keywords: | Support vector machine Linear programming Classification Variable parameter |
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