| 基于改进遗传算法的支持向量机参数优化 |
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| 引用本文： | 刘东平,单甘霖,张岐龙,段修生.基于改进遗传算法的支持向量机参数优化[J].微计算机应用,2010,31(5). |
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| 作者姓名： | 刘东平 单甘霖 张岐龙 段修生 |
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| 作者单位： | 军械工程学院光学与电子工程系,石家庄,050003 |
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| 摘 要： | 支持向量机是一种非常有前景的学习机器,但是,支持向量机参数的选取一直没有一套成熟的理论,这给支持向量机的应用带来了很大的不便.为此,本文提出了基于改进遗传算法的支持向量机的参数优化方法,利用遗传算法的全局搜索能力得到支持向量机的最优参数值.仿真实验结果表明,得到的参数可使支持向量机具有良好的泛化性能,此方法切实有效.
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| 关 键 词： | 支持向量机 改进遗传算法 参数优化 |
Parameters Optimization of Support Vector Machine based on Improved Genetic Algorithm |
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| LIU Dongping,SHAN Ganlin,ZHANG Qilong,DUAN Xiusheng.Parameters Optimization of Support Vector Machine based on Improved Genetic Algorithm[J].Microcomputer Applications,2010,31(5). |
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| Authors: | LIU Dongping SHAN Ganlin ZHANG Qilong DUAN Xiusheng |
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| Affiliation: | LIU Dongping,SHAN Ganlin,ZHANG Qilong,DUAN Xiusheng(Department of Optics , Electronics Engineering,Ordnance Engineering College,Shijiazhuang,050003,China) |
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| Abstract: | Support Vector Machines(SVM) is a promising artificial intelligence technique,but there is not a mature theoretic for choosing the parameters of SVM,which causes much discommodity to the appliance of SVM.Therefore,the Improved Genetic Algorithm was proposed utilizing the comprehensive searching ability to choose the parameters of SVM in this article in order to gain the classic parameters.Experimental results demonstrate an improvement of the generalization performance for support vector machines,which show... |
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| Keywords: | Support Vector Machine Improved Genetic Algorithm Parameters Optimization |
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