@inproceedings{2786,
  title = {Hilbertian Metrics on Probability Measures and their Application in SVM's},
  journal = {Pattern Recognition, Proceedings of th 26th DAGM Symposium},
  abstract = {The goal of this article is to
  investigate the field of Hilbertian metrics on probability
  measures. Since they are very versatile and can therefore be
  applied in various problems they are of great interest in kernel
  methods. Quit recently Tops{o}e and Fuglede introduced a family
  of Hilbertian metrics on probability measures. We give basic
  properties of the Hilbertian metrics of this family and other used
  metrics in the literature. Then we propose an extension of the
  considered metrics which incorporates structural information of
  the probability space into the Hilbertian metric. Finally we
  compare all proposed metrics in an image and text classification
  problem using histogram data.},
  volume = {3175},
  pages = {270-277},
  series = {Lecture Notes in Computer Science},
  editors = {Rasmussen, C. E., H. H. B{\"u}lthoff, M. Giese and B. Sch{\"o}lkopf},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2004},
  author = {Hein, H. and Lal, TN. and Bousquet, O.}
}