Local Rademacher complexities

Bartlett, Peter L., Bousquet, Olivier, & Mendelson, Shahar (2005) Local Rademacher complexities. The Annals of Statistics, 33(4), pp. 1497-1537.

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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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196 citations in Scopus
136 citations in Web of Science®
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ID Code: 43986
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Error bounds, concentration inequalities, data-dependent complexity, Rademacher averages
DOI: 10.1214/009053605000000282
ISSN: 0090-5364
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2005 Institute of Mathematical Statistics
Deposited On: 17 Aug 2011 22:52
Last Modified: 29 Feb 2012 14:34

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