Optimizing parameters of support vector machine’s model based on genetic algorithm

doi:10.3778/j.issn.1002-8331.2009.08.017

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (8): 55-57.DOI: 10.3778/j.issn.1002-8331.2009.08.017

• 研究、探讨 • Previous Articles Next Articles

Optimizing parameters of support vector machine’s model based on genetic algorithm

GUO Li-li^1,2,ZHAO Chun-jiang²

1.College of Engineering，China Agricultural University，Beijing 100083，China
2.National Engineering Research Center for Information Technology in Agriculture，Beijing 100037，China

Received:2008-01-25 Revised:2008-03-31 Online:2009-03-11 Published:2009-03-11
Contact: GUO Li-li

十折交叉检验的支持向量机参数优化算法

郭立力^1,2,赵春江²

1.中国农业大学工学院，北京 100083
2.国家农业信息化工程技术研究中心，北京 100037

通讯作者: 郭立力

Abstract

Abstract: Aiming at the parameters selection of support vector machine still lacks theory support and is very difficult to select．A genetic support vector machine algorithm is proposed based on genetic algorithm and tenfold crossing．The method improves the precision and efficiency of classification effectively，the most optimal parameters are obtained by genetic algorithm random search character．

Key words: Support Vector Machine（SVM）, genetic algorithm, parameters optimization

摘要： 针对支持向量机结构参数的选取在没有理论支持，选取又比较困难的情况下，提出了一种基于遗传算法和十折交叉检验相结合的遗传支持向量机（GA-SVM）算法，利用遗传算法的全局搜索特性得到SVM的最优参数值，有效提高了分类的精度和效率。

关键词: 支持向量机, 遗传算法, 参数优化

GUO Li-li^1,2,ZHAO Chun-jiang². Optimizing parameters of support vector machine’s model based on genetic algorithm[J]. Computer Engineering and Applications, 2009, 45(8): 55-57.

郭立力^1,2,赵春江². 十折交叉检验的支持向量机参数优化算法[J]. 计算机工程与应用, 2009, 45(8): 55-57.

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Optimizing parameters of support vector machine’s model based on genetic algorithm

十折交叉检验的支持向量机参数优化算法

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