Local Rademacher complexities
Bartlett, Peter L., Bousquet, Olivier , & Mendelson, Shahar (2005) Local Rademacher complexities. The Annals of Statistics, 33(4), pp. 1497-1537.
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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|Item Type:||Journal Article|
|Keywords:||Error bounds, concentration inequalities, data-dependent complexity, Rademacher averages|
|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:||18 Aug 2011 08:52|
|Last Modified:||01 Mar 2012 00:34|
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