2024-03-29T09:32:51Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/301662022-11-17T02:08:08Zhdl_2115_20053hdl_2115_145Integrated kernels and their properties1000020332471Tanaka, Akira1000010213216Imai, Hideyuki1000060205101Kudo, MineichiMiyakoshi, Masaakiopen accessKernelReproducing kernel Hilbert spaceProjection learningParameter integration007Kernel 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.Elsevier B.V.2007-11engjournal articleAMhttp://hdl.handle.net/2115/30166http://www.sciencedirect.com/science/journal/00313203https://doi.org/10.1016/j.patcog.2007.02.0140031-3203Pattern Recognition401129302938https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/30166/1/PR40-11.pdfapplication/pdf270.34 KB2007-11