福州大学学报（自然科学版）2011，Vol.39Issue(4)：527-532,538,7.DOI:CNKI:35-1117/N.20110705.1555.026
一种新的支持向量回归机的模型选择方法
A novel approach of model selection for SVR
摘要
Abstract
To solve the model selection for support vector regression, we present a novel approach based on unscented Kalman filter(UKF). First we transform the problem of model selection for SYR into a problem of nonlinear system state estimation, and introduce UKF to solve it. Compare to particle swarm algorithm, experiments on basic data sets and prediction of the smoothed monthly mean sunspot numbers show that the UKF approach has stronger optimization ability, which fully guarantees maximal generalization ability of SVR and obtains higher precision of prediction.关键词
支持向量回归机/参数选择/模型选择/无迹卡尔曼滤波Key words
support vector regression/ parameters selection/ model selection/ UKF分类
信息技术与安全科学引用本文复制引用
黄东远,陈晓云..一种新的支持向量回归机的模型选择方法[J].福州大学学报（自然科学版）,2011,39(4):527-532,538,7.基金项目
国家自然科学基金资助项目(60573076) （60573076）
福建省新世纪优秀人才资助项目(XSJRC2007-11) （XSJRC2007-11）
