最小二乘Littlewood-Paley小波支持向量机  

Least Square Littlewood-Paley Wavelet Support Vector Machine

作　　者：武方方[1] 赵银亮[1] 

机构地区：[1]西安交通大学新型计算机研究所,陕西西安710049

出　　处：《信息与控制》2005年第5期604-609,共6页Information and Control

摘　　要：基于小波分解理论和支持向量机核函数的条件,提出了一种多维允许支持向量核函数———L it-tlewood-Paley小波核函数.该核函数不仅具有平移正交性,而且可以以其正交性逼近二次可积空间上的任意曲线,从而提升了支持向量机的泛化性能.在L ittlewood-Paley小波函数作为支持向量核函数的基础上,提出了最小二乘L ittlewood-Paley小波支持向量机(LS-LPW SVM).实验结果表明,LS-LPW SVM在同等条件下比最小二乘支持向量机的学习精度要高,因而更适用于复杂函数的学习问题.*Based on the wavelet decomposition theory and conditions of the support vector kernel function, a multivariable support vector kernel function is proposed, i.e. Littlewood-Paley wavelet kernel function for SVM（ Support Vector Machine）. This function is a kind of orthonormal function, and it can approximate almost any curve in quadratic continuous integral space, thus it enhances the generalization ability of the SVM. Using Littlewood-Paley wavelet function as the support vector kernel function, the Least Square Littlewood-Paley Wavelet Support Vector Machine （LS-LPWSVM） is proposed. Experiment results show that, compared with least square support vector machine under the same conditions, the learning precision is improved by LS-LPWSVM. So, it will be more suitable for learning complicated functions.

关 键 词：支持向量机 核函数 支持向量核函数 Littlewood—Paley小波 LS-LPWSVM 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]