计算机与现代化Issue(3)：15-18,22,5.DOI:10.3969/j.issn.1006-2475.2012.03.005
基于免疫遗传算法的支持向量机参数优化及其应用
Parameter Optimization and It' s Application of Support Vector Machine Based on IGA
摘要
Abstract
Support Vector Machine(SVM) is developed on the frame of the statistical learning theory, which has been a new excellent machine learning method. SVM has solid theoretical foundation, clever algorithms. The outstanding performance of SVM depends on the choice of model parameters, including penalty parameter and kernel function parameter. In the paper, an improved Immune Genetic Algorithm (IGA) is applied to optimize the parameter of SVM. The experiment result proves that the method the paper proposed can reduce optimal time and improve the precision of classification largely compared to Grid searching when the classifier object is low dimension. When the classifier object is high dimension such as text data, the method can also reduce optimal time largely compared to Grid searching in the case of keeping same precision of classification.关键词
支持向量机/惩罚参数/核参数/免疫遗传算法Key words
support vector machine/ penalty parameter/ kernel parameter/ immune genetic algorithm分类
信息技术与安全科学引用本文复制引用
龙华..基于免疫遗传算法的支持向量机参数优化及其应用[J].计算机与现代化,2012,(3):15-18,22,5.
