2024-03-28T18:06:23Zhttps://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 propertiesTanaka, AkiraImai, HideyukiKudo, MineichiMiyakoshi, MasaakiKernelReproducing 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.Journal Articleapplication/pdfhttp://hdl.handle.net/2115/30166https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/30166/1/PR40-11.pdf0031-3203Pattern Recognition4011293029382007-11enginfo:doi/10.1016/j.patcog.2007.02.014author