计算机工程与科学2012，Vol.34Issue(9)：113-117,5.DOI:10.3969/j.issn.1007-130X.2012.09.021
基于多蚁群算法的支持向量回归机参数选择方法
Parameters Selection of Support Vector Regression Machine Based on Multi-Ant Colony Optimization
摘要
Abstract
The kernel function in the Support Vector Regression (SVR) machine has a great influence on the quality of model. Currently, however, every kernel has its advantages and disadvantages. Based on the fact that the regression accuracy and generalization performance of the SVR models depends on a proper setting of its parameters, the continuous multi-ant colony optimization (MACO) method based on gridding partition is applied in mixture-kernels SVR parameters. The cross-validation error is used as the fitness function of MACO. The optimal values in ant system were reflected by the 5 parameters of SVR. Simulation results show that the optimal selection approach based on MACO-SVR has good robustness and strong global search capability. The method used for the research of modeling in the traffic flow forecast obtains higher accuracy than the models constructed with the Genetic Algorithm.关键词
蚁群算法/支持向量回归机/核函数/参数优化Key words
ant colony optimization/ support vector regression machine/ kernel function/ parameter optimization分类
信息技术与安全科学引用本文复制引用
陈宝文,谭旭..基于多蚁群算法的支持向量回归机参数选择方法[J].计算机工程与科学,2012,34(9):113-117,5.基金项目
国家自然科学基金资助项目(71101096) （71101096）
广东省自然科学基金资助项目(10451802904005327) （10451802904005327）
