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  Risk-Based Generalizations of f-divergences

García-García, D., von Luxburg, U., & Santos-Rodríguez, R. (2011). Risk-Based Generalizations of f-divergences. In L. Getoor (Ed.), 28th International Conference on Machine Learning (ICML 2011) (pp. 417-424). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB2E-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-0A9F-1
Genre: Conference Paper

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http://www.icml-2011.org/ (Table of contents)
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 Creators:
García-García, D, Author
von Luxburg, U1, Author              
Santos-Rodríguez, R, Author
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: We derive a generalized notion of f-divergences, called (f,l)-divergences. We show that this generalization enjoys many of the nice properties of f-divergences, although it is a richer family. It also provides alternative definitions of standard divergences in terms of surrogate risks. As a first practical application of this theory, we derive a new estimator for the Kulback-Leibler divergence that we use for clustering sets of vectors.

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 Dates: 2011-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: GarciaGarciavS2011
 Degree: -

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Title: 28th International Conference on Machine Learning (ICML 2011)
Place of Event: Bellevue, WA, USA
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Title: 28th International Conference on Machine Learning (ICML 2011)
Source Genre: Proceedings
 Creator(s):
Getoor, L, Editor
Scheffer, T, Author
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 417 - 424 Identifier: ISBN: 978-1-450-30619-5