Name
Abstract | ||
|---|---|---|
A novel admissible support vector kernel, namely the wavelet kernel satisfying wavelet frames, is presented based on the wavelet theory. The wavelet kernel can approximate arbitrary functions, and is especially suitable for local signal analysis, hence the generalization ability of the support vector machines (SVM) is improved. Based on the wavelet kernel and the least squares support vector machines, the least squares wavelet support vector machines (LS-WSVM) are constructed. In order to validate the performance of the wavelet kernel, LS-WSVM is applied to a nonlinear system identification problem, and the computational process is compared with that of the Gaussian kernel. The results show that the wavelet kernel is more efficient than the Gaussian kernel. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11427445_71 | ISNN (2) |
Keywords | Field | DocType |
approximate arbitrary function,squares wavelet support vector,support vector machine,wavelet theory,wavelet frame,computational process,squares support vector machine,novel admissible support vector,gaussian kernel,wavelet kernel,nonlinear system identification,least square,support vector,least squares support vector machine,signal analysis,satisfiability | Radial basis function kernel,Computer science,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,String kernel,Wavelet packet decomposition,Least squares support vector machine,Pattern recognition,Algorithm,Kernel method,Variable kernel density estimation,Machine learning | Conference |
Volume | ISSN | ISBN |
3497 | 0302-9743 | 3-540-25913-9 |
Citations | PageRank | References |
3 | 0.58 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenhua Yu | 1 | 3 | 1.26 |
| CAI Yuan-li | 2 | 29 | 6.90 |