最小二乘Littlewood-Paley小波支持向量机

Least Square Littlewood-Paley Wavelet Support Vector Machine

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.