PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A survey of cross-validation procedures for model selection
Sylvain Arlot and Alain Celisse
Statistics Surveys Volume 4, pp. 40-79, 2010. ISSN 1935-7516

Abstract

Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results. As a conclusion, guidelines are provided for choosing the best cross-validation procedure according to the particular features of the problem in hand.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6812
Deposited By:Sylvain Arlot
Deposited On:10 April 2010