Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
531717 | Pattern Recognition | 2007 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi,