WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION

被引：0
|
作者：
Tong Yubing Yang Dongkai Zhang Qishan (Dept of Electronic Information Engineering
机构：
来源：
Journal of Electronics(China) | 2006年 / 04期
关键词：
Wavelet kernel function; Support Vector Machines (SVM); Sparse approximation; Quadratic Programming (QP);
D O I：
暂无
中图分类号：
TP18 [人工智能理论]; TN911.7 [信号处理];
学科分类号：
0711 ; 080401 ; 080402 ; 081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with bet-ter sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target fun-citon with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation ex-periment show the feasibility and validity of wavelet kernel support vector machines.
引用
收藏
页码：539 / 542
页数：4
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