Least squares support vector machine based on continuous wavelet kernel

被引：0
|
作者：
Wen, XJ [1 ]
Cai, Y [1 ]
Xu, XM [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Automat Dept, Shanghai 200030, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 1, PROCEEDINGS | 2005年 / 3496卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the continuous wavelet transform theory and conditions of the admissible support vector kernel, a novel notion of multidimensional wavelet kernels is proposed for Least Squares Support Vector Machine (LS-WSVM) for pattern recognition and function estimation. Theoretic analysis of the wavelet kernel is discussed in detail. The good approximation property of wavelet kernel function enhances the generalization ability of LS-WSVM method and some experimental results are presented to illustrate the effectiveness and feasibility of the proposed method.
引用
收藏
页码：843 / 850
页数：8
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