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A Hilbert Space Embedding for Distributions

2007

Poster

ei


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.

Author(s): Smola, AJ. and Gretton, A. and Song, L. and Schölkopf, B.
Journal: Proceedings of the 10th International Conference on Discovery Science (DS 2007)
Volume: 10
Pages: 40-41
Year: 2007
Month: October
Day: 0

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
DOI: 10.1007/978-3-540-75488-6_5
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@poster{4644,
  title = {A Hilbert Space Embedding for Distributions},
  author = {Smola, AJ. and Gretton, A. and Song, L. and Sch{\"o}lkopf, B.},
  journal = {Proceedings of the 10th International Conference on Discovery Science (DS 2007)},
  volume = {10},
  pages = {40-41},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2007},
  month_numeric = {10}
}