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  PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering

Seldin, Y. (2009). PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering. In the proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009), 472-479.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C54B-8 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C54C-6
Genre: Konferenzbeitrag

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 Urheber:
Seldin, Y1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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Schlagwörter: -
 Zusammenfassung: We derive a PAC-Bayesian generalization bound for density estimation. Similar to the PAC-Bayesian generalization bound for classification, the result has the appealingly simple form of a tradeoff between empirical performance and the KL-divergence of the posterior from the prior. Moreover, the PAC-Bayesian generalization bound for classification can be derived as a special case of the bound for density estimation. To illustrate a possible application of our bound we derive a generalization bound for co-clustering. The bound provides a criterion to evaluate the ability of co-clustering to predict new co-occurrences, thus introducing a supervised flavor to this traditionally unsupervised task.

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 Datum: 2009-04
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://jmlr.csail.mit.edu/proceedings/papers/v5/seldin09a.html
BibTex Citekey: 6592
 Art des Abschluß: -

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Titel: 12th International Conference on Artificial Intelligence and Statistics
Veranstaltungsort: Clearwater Beach, FL, USA
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Titel: In the proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Cambridge, MA, USA : MIT Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 472 - 479 Identifikator: -