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Selecting an optimal set of parameters using an Akaike like criterion

University of Groningen, Department of Computing Science,

P.O. Box 800, NL-9700 AV Groningen, The Netherlands,

phone: +31.50.363 3940 - fax: +31.50.363 38005
- e-mail: rudy@cs.rug.nl

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Abstract

The selection of an optimal set of parameters from a larger one is a well
known identification problem in classification or clustering algorithms. The
Akaike criterion has been developed to estimate the (Markov) order in auto
regressive models. This criterion, which by itself extends the maximum
likelihood method to test composite hypotheses, is replaced by the
*Modified Information Criterion* (MIC). This criterion balances bias
caused insufficient modeling and the additional variance caused by
superfluous parameters. Using this criterion the probability of selecting a
model with too many parameters can, without the need of extensively
evaluating the bias/variance syndrome, a priori be chosen.

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Published

*Selecting an optimal set of parameters using an Akaike like criterion*,
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