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Journal Article

Local Rademacher Complexities

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83824

Bousquet,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bartlett, P., Bousquet, O., & Mendelson, S. (2005). Local Rademacher Complexities. The Annals of Statistics, 33(4), 1497-1537. Retrieved from http://projecteuclid.org/Dienst/UI/1.0/Summarize/euclid.aos/1123250221.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D4A3-9
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.