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  Local Rademacher Complexities

Bartlett, P., Bousquet, O., & Mendelson, S. (2005). Local Rademacher Complexities. The Annals of Statistics, 33(4), 1497-1537.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D4A3-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D79A-C
Genre: Journal Article

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 Creators:
Bartlett, P, Author
Bousquet, O1, 2, Author              
Mendelson, S, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

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 Dates: 2005-08
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 2000
 Degree: -

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Title: The Annals of Statistics
Source Genre: Journal
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Publ. Info: Cleveland, Ohio [etc] : Institute of Mathematical Statistics [etc.]
Pages: - Volume / Issue: 33 (4) Sequence Number: - Start / End Page: 1497 - 1537 Identifier: ISSN: 0090-5364
CoNE: https://pure.mpg.de/cone/journals/resource/954925461135