DocumentCode
442106
Title
Sparse approximation based on wavelet kernel support vector machines
Author
Yang, Dong-Kai ; Tong, Yu-Bing ; Zhang, Qi-Shan
Author_Institution
Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing, China
Volume
7
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
4249
Abstract
For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel support vector machines, which can converge to minimum error with better sparsity. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.
Keywords
approximation theory; convergence; source separation; sparse matrices; support vector machines; wavelet transforms; convergence; sparse approximation; support vector machines; wavelet approximation; wavelet kernel function; Approximation algorithms; Dictionaries; Discrete wavelet transforms; Electronic mail; Kernel; Matching pursuit algorithms; Packaging machines; Signal resolution; Support vector machines; Wavelet analysis; Sparse Approximation; Support Vector Machine; Wavelet Kernel Function;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527683
Filename
1527683
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