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. One of central topics of kernel machines is model selection, especially selection of a kernel or its parameters. In this paper, we consider a class of kernels that forms a monotonic classes of reproducing kernel Hilbert spaces with an invariant metric and show that the kernel corresponding to the smallest reproducing kernel Hilbert space including an unknown true function gives the optimal model for the unknown true function. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-89689-0_57 | SSPR/SPR |
Keywords | Field | DocType |
optimal kernel,optimal model,smallest reproducing kernel hilbert,kernel machine,pattern recognition,central topic,monotonic class,unknown true function,reproducing kernel hilbert space,information science,invariant metric,model selection | Pattern recognition,Algebra,Radial basis function kernel,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,String kernel,Variable kernel density estimation,Mathematics,Reproducing kernel Hilbert space,Kernel (statistics) | Conference |
Volume | ISSN | Citations |
5342 | 0302-9743 | 12 |
PageRank | References | Authors |
1.00 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Tanaka | 1 | 38 | 12.20 |
| Hideyuki Imai | 2 | 103 | 25.08 |
| Mineichi Kudo | 3 | 927 | 116.09 |
| Masaaki Miyakoshi | 4 | 99 | 20.27 |