Name
Abstract | ||
|---|---|---|
Learning based on kernel machines is widely known as a powerful tool for various fields of information science such as pattern recognition and regression estimation. The efficacy of the model in kernel machines depends on the distance between the unknown true function and the linear subspace, specified by the training data set, of the reproducing kernel Hilbert space corresponding to an adopted kernel. In this paper, we propose a framework for the model selection of kernel-based learning machines, incorporating a class of kernels with an invariant metric. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11815921_95 | SSPR/SPR |
Keywords | Field | DocType |
training data,kernel machine,pattern recognition,linear subspace,kernel-based learning machine,regression estimation,powerful tool,reproducing kernel hilbert space,information science,model selection | Radial basis function kernel,Kernel embedding of distributions,Computer science,Algorithm,Kernel principal component analysis,Tree kernel,Polynomial kernel,String kernel,Variable kernel density estimation,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
4109 | 0302-9743 | 3-540-37236-9 |
Citations | PageRank | References |
1 | 0.46 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Tanaka | 1 | 38 | 12.20 |
| Masashi Sugiyama | 2 | 3353 | 264.24 |
| Hideyuki Imai | 3 | 103 | 25.08 |
| Mineichi Kudo | 4 | 927 | 116.09 |
| Masaaki Miyakoshi | 5 | 99 | 20.27 |