Name
Abstract | ||
|---|---|---|
One of central topics of kernel machines in the field of machine learning is a model selection, especially a selection of a kernel or its parameters. In our previous work, we discussed a class of kernels forming a class of nested reproducing kernel Hilbert spaces with an invariant metric; and proved that the kernel corresponding to the smallest reproducing kernel Hilbert space, including an unknown true function, gives the optimal model. In this paper, we consider a class of kernels forming a class of nested reproducing kernel Hilbert spaces whose metrics are not always invariant and show that a similar result to the invariant case is not obtained by providing a counter example using a class of Gaussian kernels. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5946733 | ICASSP |
Keywords | Field | DocType |
Gaussian processes,Hilbert spaces,learning (artificial intelligence),Gaussian kernels,RKHS,invariant metrics,kernel Hilbert spaces,kernel machines,machine learning,model selection,generalization ability,kernel machine,metric,model space,reproducing kernel Hilbert space | Mathematical optimization,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Representer theorem,String kernel,Variable kernel density estimation,Reproducing kernel Hilbert space,Mathematics,Kernel (statistics) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 7 | 0.65 |
References | Authors | |
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 |