@inproceedings{4426,
  title = {A Kernel Approach to Comparing Distributions},
  journal = {Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07)},
  booktitle = {Proceedings of the 22. AAAI Conference on Artificial Intelligence},
  abstract = {We describe a technique for comparing distributions without
  the need for density estimation as an intermediate step.
  Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to
  construct a two-sample test, which is used for determining
  whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.},
  pages = {1637-1641},
  publisher = {AAAI Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {Association for the Advancement of Artificial Intelligence},
  school = {Biologische Kybernetik},
  address = {Menlo Park, CA, USA},
  month = jul,
  year = {2007},
  slug = {4426},
  author = {Gretton, A. and Borgwardt, KM. and Rasch, M. and Sch{\"o}lkopf, B. and Smola, AJ.},
  month_numeric = {7}
}