Name
Abstract | ||
|---|---|---|
Support vector machines(SVM) have been introduced for pattern recognition and regression. But it was limited by the time consuming and the choice of kernel function in practical application. Motivated by the theory of multi-scale representations of signals and wavelet transforms, this paper presents a way for building a wavelet-based reproducing kernel Hilbert spaces (RKHS) which is a multiresolution scale subspace and its associate scaling kernel for least squares support vector machines (LS-SVM). The scaling kernel is constructed by using a scaling function with its different dilations and translations. Results on several approximation problems illustrate that the LS-SVM with scaling kernel can approximate arbitrary signal with multi-scale and owns better approximation performance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76631-5_37 | MICAI |
Keywords | Field | DocType |
pattern recognition,reproducing kernel hilbert space,wavelet transform,least squares support vector machine,support vector machine,kernel function | Radial basis function kernel,Pattern recognition,Least squares support vector machine,Kernel embedding of distributions,Computer science,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Kernel method,Variable kernel density estimation,Kernel (statistics) | Conference |
Volume | Issue | ISSN |
4827 LNAI | null | 0302-9743 |
ISBN | Citations | PageRank |
3-540-76630-8 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyang Mu | 1 | 0 | 0.34 |
| Taiyi Zhang | 2 | 5 | 0.87 |
| Yatong Zhou | 3 | 28 | 5.72 |