Parameters Optimization of Support Vector Regression based on Cultural Algorithm

被引：0

作者：

Yang, Kaifan ^{[1
]}

机构：

[1] Shaanxi Univ Technol, Apartment Math, Hanzhong, Shaanxi, Peoples R China

来源：

2011 AASRI CONFERENCE ON APPLIED INFORMATION TECHNOLOGY (AASRI-AIT 2011), VOL 1 | 2011年

关键词：

support vector regression (SVR); cultural Algorithm (CA); parameters optimization;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Parameters are important factors to affect the performance of SVR. Parameters optimization of SVR based on cultural algorithm is presented to improve prediction accuracy and generalization ability. Optimal rule is the least mean square error of samples, and the parameters of SVR are optimized based on adaptive capacity of cultural algorithm. The simulation results show that the algorithm can accelerate the pace of the search parameters and improve prediction accuracy of SVR, It has good robustness and strong global search capability.

引用

页码：218 / 221

页数：4