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Conference Paper

A Hilbert Space Embedding for Distributions

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Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Smola, A., Gretton, A., Song, L., & Schölkopf, B. (2007). A Hilbert Space Embedding for Distributions. In V. Corruble, M. Takeda, & E. Suzuki (Eds.), Discovery Science: 10th International Conference, DS 2007 Sendai, Japan, October 1-4, 2007 (pp. 40-41). Berlin, Germany: Springer.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CB99-4
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.