The nature of statistical learning theory
The nature of statistical learning theory
Geometry and invariance in kernel based methods
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Online Least Squares Support Vector Machines Based on Wavelet and Its Applications
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Least squares support vector machine on gaussian wavelet kernel function set
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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The kernel function of support vector machine (SVM) is an important factor for the learning result of SVM. Based on the wavelet decomposition and conditions of the support vector kernel function, Littlewood-Paley wavelet kernel function for SVM is proposed. This function is a kind of orthonormal function, and it can simulate almost any curve in quadratic continuous integral space, thus it enhances the generalization ability of the SVM. According to the wavelet kernel function and the regularization theory, Least squares Littlewood-Paley wavelet support vector machine (LS-LPWSVM) is proposed to simplify the process of LPWSVM. The LS-LPWSVM is then applied to the regression analysis and classifying. Experiment results show that the precision is improved by LS-LPWSVM, compared with LS-SVM whose kernel function is Gauss function.