@techreport{2815,
  title = {Hilbertian Metrics and Positive Definite Kernels on Probability Measures},
  abstract = {We investigate the problem of defining Hilbertian metrics resp.
  positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good
  results in text classification and has a wide range of possible
  applications. In this paper we extend the two-parameter family of
  Hilbertian metrics of Topsoe such that it now includes all
  commonly used Hilbertian metrics on probability measures. This
  allows us to do model selection among these metrics in an elegant
  and unified way. Second we investigate further our approach to
  incorporate similarity information of the probability space into
  the kernel. The analysis provides a better understanding of these
  kernels and gives in some cases a more efficient way to compute
  them. Finally we compare all proposed kernels in two text and one
  image classification problem.},
  number = {126},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, T{\"u}bingen, Germany},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2004},
  author = {Hein, M. and Bousquet, O.},
  month_numeric = {7}
}