@article{2839,
  title = {Maximal Margin Classification for Metric Spaces},
  journal = {Journal of Computer and System Sciences},
  abstract = {In order to apply the maximum margin method in arbitrary metric
  spaces, we suggest to embed the metric space into a Banach or
  Hilbert space and to perform linear classification in this space.
  We propose several embeddings and recall that an isometric embedding
  in a Banach space is always possible while an isometric embedding in
  a Hilbert space is only possible for certain metric spaces. As a
  result, we obtain a general maximum margin classification
  algorithm for arbitrary metric spaces (whose solution is
  approximated by an algorithm of Graepel.
  Interestingly enough, the embedding approach, when applied to a metric
  which can be embedded into a Hilbert space, yields the SVM
  algorithm, which emphasizes the fact that its solution depends on
  the metric and not on the kernel. Furthermore we give upper bounds
  of the capacity of the function classes corresponding to both
  embeddings in terms of Rademacher averages. Finally we compare the
  capacities of these function classes directly.},
  volume = {71},
  number = {3},
  pages = {333-359},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2005},
  slug = {2839},
  author = {Hein, M. and Bousquet, O. and Sch{\"o}lkopf, B.},
  month_numeric = {10}
}