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Integrated kernels and their properties
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DOI：10.1016/j.patcog.2007.02.014.png)
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Abstract
En 中文
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. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
kernel
reproducing kernel Hilbert space
projection learning
parameter integration
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