An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels

We describe how to use Schoenberg's theorem for a radial kernel combined with existing bounds on the approximation error functions for Gaussian kernels to obtain a bound on the approximation error function for the radial kernel. The result is applied to ...

Reproducing Kernel Hilbert Spaces (RKHSs) are a very useful and powerful tool of functional analysis with application in many diverse paradigms, such as multivariate statistics and machine learning. Fractal interpolation, on the other hand, is a ...

The problem of model selection for support vector machines using the RBF kernels is considered. We introduce a geometric view on maximizing kernel polarization, which intuitively shows why we choose kernel polarization as the criterion for kernel ...