The nature of statistical learning theory
The nature of statistical learning theory
An equivalence between sparse approximation and support vector machines
Neural Computation
The covering number in learning theory
Journal of Complexity
Capacity of reproducing kernel spaces in learning theory
IEEE Transactions on Information Theory
The Journal of Machine Learning Research
A new kernel-based approach for linear system identification
Automatica (Journal of IFAC)
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Reproducing kernel Hilbert spaces are an important family of function spaces and play useful roles in various blanches of analysis and applications including the kernel machine learning.When the domain of definition is compact,They can be characterized as the image of the square root of an integral operator, by means of the Mercer theorem The purpose of this paper is to extend the Mercer theorem to noncompact domains and to establish a functional analysis characterization of the reproducing kernel Hilbert spaces on general domains.