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A Hilbert Space Embedding for Distributions

  • Alex Smola
  • Arthur Gretton
  • Le Song
  • Bernhard Schölkopf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4755)

Abstract

While kernel methods are the basis of many popular techniques in supervised learning, they are less commonly used in testing, estimation, and analysis of probability distributions, where information theoretic approaches rule the roost. However it becomes difficult to estimate mutual information or entropy if the data are high dimensional.

Keywords

Mutual Information Mixture Model Supervise Learning Kernel Method Exponential Family 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alex Smola
    • 1
  • Arthur Gretton
    • 2
  • Le Song
    • 1
  • Bernhard Schölkopf
    • 2
  1. 1.NICTA and ANU, Northbourne Avenue 218, Canberra 0200 ACTAustralia
  2. 2.MPI for Biological Cybernetics, Spemannstr. 38, 72076 TübingenGermany

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