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
Optimal Kernel in a Class of Kernels with an Invariant Metric
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Learning based on kernel machines is widely known as a powerful tool for various fields of information science such as pattern recognition and regression estimation. The efficacy of the model in kernel machines depends on the distance between the unknown true function and the linear subspace, specified by the training data set, of the reproducing kernel Hilbert space corresponding to an adopted kernel. In this paper, we propose a framework for the model selection of kernel-based learning machines, incorporating a class of kernels with an invariant metric.