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
Integral operators generated by multi-scale kernels
Journal of Complexity
Application of integral operator for regularized least-square regression
Mathematical and Computer Modelling: An International Journal
Dependent wild bootstrap for degenerate U- and V-statistics
Journal of Multivariate Analysis
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Reproducing kernel Hilbert spaces are an important family of function spaces and play useful roles in various branches 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.