| 基于RBF核的SVM核参数优化算法 |
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| 引用本文： | 董国君,哈力木拉提·买买提,余辉.基于RBF核的SVM核参数优化算法[J].新疆大学学报(理工版),2009,26(3):355-358,363. |
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| 作者姓名： | 董国君 哈力木拉提·买买提 余辉 |
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| 作者单位： | 新疆大学信息科学与工程学院,新耩乌鲁木齐830046 |
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| 摘 要： | SVM是一种新型的机器学习方法，其分类性能的优劣主要受核函数及核参数的影响，国内外学者针对SVM核参数的选择已提出许多算法．本文首先分析TRBF核参数对SVM分类性能的影响，然后又对比分析了目前存在的几种基于RBF核的SVM核参数选择方法．通过实验，发现使用遗传算法选择核参数的SVM有比较快的搜索速度．
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| 关 键 词： | 支持向量机 RBF核 遗传算法 |
Algorithms of Optimizing SVM'S Kernel Parameters with RBF Kernel |
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| DONG Guo-Jun,Halmurat Maimait,YU Hui.Algorithms of Optimizing SVM'S Kernel Parameters with RBF Kernel[J].Journal of Xinjiang University(Science & Engineering),2009,26(3):355-358,363. |
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| Authors: | DONG Guo-Jun Halmurat Maimait YU Hui |
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| Institution: | (College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830036, China) |
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| Abstract: | Support vector machine（SVM） is a new kind of machine learning methods, whose classification performance is affected by kernel function and kernel parameters. Nowadays some researchers have proposed many algorithms about choosing kernel parameters. This paper emphasizes on analyzing kernel parameters choice algorithms of SVM with RBF kernel. The paper finds that genetic algorithm which is used in choosing kernel parameters could improve search velocity of SVM. |
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| Keywords: | support vector machine rbf kernel genetic algorithm |
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