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Local Rademacher complexities

Bartlett, Peter L.; Bousquet, Olivier; Mendelson, Shahar


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.

CollectionsANU Research Publications
Date published: 2005
Type: Journal article
Source: The Annals of Statistics
DOI: 10.1214/009053605000000282


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