PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improved risk tail bounds for on-line algorithms
Nicolò Cesa-Bianchi and Claudio Gentile
IEEE Transactions on Information Theory Volume 54, Number 1, pp. 386-390, 2008.

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Abstract

Tight bounds are derived on the risk of models in the ensemble generated by incremental training of an arbitrary learning algorithm. The result is based on proof techniques that are remarkably different from the standard risk analysis based on uniform convergence arguments, and improves on previous bounds published by the same authors.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3700
Deposited By:Nicolò Cesa-Bianchi
Deposited On:14 February 2008

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