Local Rademacher Complexities

Bartlett, P; Bousquet, O; Mendelson, S

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Local Rademacher Complexities

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

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https://projecteuclid.org/download/pdfview_1/euclid.aos/1123250221
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Citation

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

Cite as: https://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.