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基于遗传算法的支持向量机分类器模型参数优化
引用本文:陈果.基于遗传算法的支持向量机分类器模型参数优化[J].机械科学与技术(西安),2007,26(3):347-350.
作者姓名:陈果
作者单位:南京航空航天大学民航学院,南京210016
摘    要:建立在统计学习理论和结构风险最小原则上的支持向量机在理论上保证了模型的最大泛化能力,因此与建立在经验风险最小原则上的神经网络模型相比,理论上更为完善。本文运用支持向量机建立模式识别分类器模型,研究影响模型分类能力的相关参数,在分析参数对分类器识别精度的影响基础上,提出用遗传算法建立支持向量机分类器模型的参数自适应优化算法。最后,用算例表明了本文算法的正确有效性。

关 键 词:支持向量机  模式识别  遗传算法  优化
文章编号:1003-8728(2007)03-0347-04
修稿时间:2005-12-05

Optimizing the Parameters of Support Vector Machine's Classifier Model Based on Genetic Algorithm
Chen Guo.Optimizing the Parameters of Support Vector Machine''''s Classifier Model Based on Genetic Algorithm[J].Mechanical Science and Technology,2007,26(3):347-350.
Authors:Chen Guo
Affiliation:College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016
Abstract:The support vector machine(SVM),which is based on the statistical learning theory(SLT) and the structural risk minimum principle,guarantees the largest generalization ability of a model.It is,therefore,theoretically more perfect than the neural network model that is based on the empirical risk minimum principle.The paper established the pattern recognition classifier model and studied the parameters that influence the classifier model′s classification ability;on the basis of analyzing the parameter′s influence on the classifier′s recognition accuracy,it proposed the self-adaptive optimization algorithm for the SVM classifier model using genetic algorithm.Finally,calculation instances show the effectiveness of the optimization algorithm.
Keywords:support vector machine(SVM)  pattern recognition  genetic algorithm  optimization
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