Abstract

During the last years support vector machines (SVMs) have been successfully applied in situations where the input space X is not necessarily a subset of Examples include SVMs for the analysis of histograms or colored images, SVMs for text classification and web mining, and SVMs for applications from computational biology using, e.g., kernels for trees and graphs. Moreover, SVMs are known to be consistent to the Bayes risk, if either the input space is a complete separable metric space and the reproducing kernel Hilbert space (RKHS) H is dense, or if the SVM uses a universal kernel k. So far, however, there are no kernels of practical interest known that satisfy these assumptions, if We close this gap by providing a general technique based on Taylor-type kernels to explicitly construct universal kernels on compact metric spaces which are not subset of Rd . We apply this technique for the following special cases: universal kernels on the set of probability measures, universal kernels based on Fourier transforms, and universal kernels for signal processing.