Optimization of Combined Kernel Function for SVM based on Large Margin Learning Theory

被引：5
|
作者：
Lu, Mingzhu [1 ]
Chen, C. L. Philip [1 ]
Huo, Jianbing [2 ]
Wang, Xizhao [3 ]
机构：
[1] Univ Texas San Antonio, Dept Elect & Comp Engn, San Antonio, TX USA
[2] Peoples Bank China, Shijiazhuang Cent Branch, Shijiazhuang, Peoples R China
[3] Hebei Univ, Coll Math & Comp & Sci, Baoding, Peoples R China
来源：
2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6 | 2008年
关键词：
combined kernel function; SVM; large margin learning; genetic algorithm; optimization;
D O I：
10.1109/ICSMC.2008.4811301
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Kernel function plays a very important role in the performance of SVM. In order to improve generalization capability of SVM classifier, this paper proposes a new mechanism to optimize the parameters of combined kernel function by using large margin learning theory and a genetic algorithm, which aims to search the optimal parameters for the combined kernel function. This approach leads SVM to attain the maximum margin in the training dataset. The Combined kernel function and the parameters obtained by the proposed approach leads to a better performance and results in a better SVM classifier. Both numerical simulation results and theoretical analysis show the effectiveness and feasibility of the proposed approach.
引用
收藏
页码：353 / +
页数：2
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