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

Ingo Steinwart, Don R. Hush, Clint Scovel

Abstract: Although Gaussian radial basis function (RBF) kernels are one of the most often used kernels in modern machine learning methods such as support vector machines (SVMs), little is known about the structure of their reproducing kernel Hilbert spaces (RKHSs). In this work, two distinct explicit descriptions of the RKHSs corresponding to Gaussian RBF kernels are given and some consequences are discussed. Furthermore, an orthonormal basis for these spaces is presented. Finally, it is discussed how the results can be used for analyzing the learning performance of SVMs.