Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (18): 36-38.

• 理论研究 • Previous Articles     Next Articles

Data fitting experiments of LS-WSVM

XIAN Guang-ming   

  1. Department of Computer Engineering,Nanhai Campus,South China Normal University,Foshan,Guangdong 528225,China
  • Received:2007-09-17 Revised:2008-01-10 Online:2008-06-21 Published:2008-06-21
  • Contact: XIAN Guang-ming

基于最小二乘小波支持向量机的数据拟合实验

冼广铭   

  1. 华南师范大学 南海校区 计算机工程系,广东 佛山 528225
  • 通讯作者: 冼广铭

Abstract: The used SVM kernel function can not approach to any object function in regression.Aiming at the problem,the construction model of Least Square Wavelet Support Vector Machine(LS-WSVM) is proposed based on conditions of the support vector kernel function and wavelet frame theory.Compared with standard SVM and LS-SVM under the same conditions,experimental results show that LS-WSVM has more excellent performance of feature abstraction in regression and its fitting result is more accurate.

摘要: 针对目前使用的SVM核函数在回归中不能逼近任意目标函数的问题,在支持向量机的核方法和小波框架理论的基础上,提出了LS-WSVM结构模型。该模型在LS-SVM中使用一种新的由小波构成的SVM核函数。实验结果表明,与标准的SVM及LS-SVM比较起来,在同等条件下,LS-WSVM在函数回归方面LS-WSVM具有优良的逼近性能,拟合效果更为细腻。