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人工鱼群算法在SVM参数优化选择中的应用
引用本文:高雷阜,赵世杰,高晶.人工鱼群算法在SVM参数优化选择中的应用[J].计算机工程与应用,2013(23):86-90.
作者姓名:高雷阜  赵世杰  高晶
作者单位:辽宁工程技术大学理学院,辽宁阜新123000
基金项目:辽宁省教育厅基金项目(No.L2012105).
摘    要:针对支持向量机的参数优化缺乏理论支持,而SVM交叉检验法选取又较为费时的情况下,提出了基于人工鱼群算法的支持向量机参数优化选取算法,并以SVM分类预测准确率最大为优化原则,利用人工鱼群算法的较好并行性和较强的全局寻优能力,以实现最优目标并得到SVM的最优参数组合。数值实验结果表明:人工鱼群算法在SVM参数优化选取中具有更快的寻优性能,同时具有较高的分类准确率。该方法具有较好的并行性和较强的全局寻优能力。

关 键 词:支持向量机  人工鱼群算法  参数优化  遗传算法

Application of artificial fish-swarm algorithm in SVM parameter optimization selection.
GAO Leifu,ZHAO Shijie,GAO Jing.Application of artificial fish-swarm algorithm in SVM parameter optimization selection.[J].Computer Engineering and Applications,2013(23):86-90.
Authors:GAO Leifu  ZHAO Shijie  GAO Jing
Affiliation:( College of Science, Liaoning Technical University, Fuxin, Liaoning 123000, China)
Abstract:As considering that the parameter optimization of support vector machine lacks theory support and the SVM cross-validation method spends lots of time on selecting parameters, the parameter optimization selection method of support vec- tor machine is proposed based on artificial fish-swarm algorithm. This method puts the SVM classification prediction accuracy rate as the optimization principle and uses the better parallelism of artificial fish-swarm algorithm and the stronger global optimi- zation ability to achieve the optimal target and obtain optimal parameter combination of SVM. The results of numerical value experi- ments show that the artificial fish-swarm algorithm has faster performance optimization and higher classification accuracy rate in SVM parameters' optimization selection. This method has the better parallelism and the stronger global optimization ability.
Keywords:support vector machine  artificial fish-swarm algorithm  parameter optimization  genetic algorithm
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