最小二乘Littlewood-Paley小波支持向量机

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——Littlewood-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Littlewood-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Littlewood-Paley小波支持向量机(LS-LPWSVM)．实验结果表明，LS-LPWSVM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．

Least Square Littlewood-Paley Wavelet Support Vector Machine

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.