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The AIC criterion and symmetrizing the Kullback-Leibler divergence

Seghouane, Abd-Krim; Amari, Shun-ichi

Description

The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as an asymptotically unbiased estimator of a function used for ranking candidate models which is a variant of the Kullback-Leibler divergence between the true model and the approximating candidate model. Despite the Kullback-Leibler's computational and theoretical advantages, what can become inconvenient in model selection applications is their lack of symmetry. Simple examples can show that...[Show more]

CollectionsANU Research Publications
Date published: 2007
Type: Journal article
URI: http://hdl.handle.net/1885/52575
Source: IEEE Transactions on Neural Networks
DOI: 10.1109/TNN.2006.882813

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