The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Incremental projection learning for optimal generalization
Neural Networks
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Subspace Information Criterion for Model Selection
Neural Computation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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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.