Name
Abstract | ||
|---|---|---|
Kernel machines are widely considered to be powerful tools in various fields of information science. By using a kernel, an unknown target is represented by a function that belongs to a reproducing kernel Hilbert space (RKHS) corresponding to the kernel. The application area is widened by enlarging the RKHS such that it includes a wide class of functions. In this study, we demonstrate a method to perform this by using parameter integration of a parameterized kernel. Some numerical experiments show that the unresolved problem of finding a good parameter can be neglected. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.patcog.2007.02.014 | Pattern Recognition |
Keywords | Field | DocType |
numerical experiment,kernel machine,good parameter,parameterized kernel,powerful tool,parameter integration,reproducing kernel hilbert space,unknown target,application area,information science,integrated kernel,kernel | Radial basis function kernel,Tree kernel,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,String kernel,Mathematical optimization,Pattern recognition,Kernel embedding of distributions,Algorithm,Variable kernel density estimation,Mathematics,Reproducing kernel Hilbert space | Journal |
Volume | Issue | ISSN |
40 | 11 | Pattern Recognition |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
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 |