D. J. Hand (auth.), P. Cheeseman, R. W. Oldford (eds.)'s Selecting Models from Data: Artificial Intelligence and PDF

By D. J. Hand (auth.), P. Cheeseman, R. W. Oldford (eds.)

ISBN-10: 0387942815

ISBN-13: 9780387942810

ISBN-10: 1461226600

ISBN-13: 9781461226604

This quantity is a range of papers awarded on the Fourth foreign Workshop on man made Intelligence and data held in January 1993. those biennial workshops have succeeded in bringing jointly researchers from man made Intelligence and from records to debate difficulties of mutual curiosity. The alternate has broadened study in either fields and has strongly encour­ elderly interdisciplinary paintings. The subject ofthe 1993 AI and facts workshop was once: "Selecting types from Data". The papers during this quantity attest to the range of methods to version choice and to the ubiquity of the matter. either facts and synthetic intelligence have independently built methods to version choice and the corresponding algorithms to enforce them. yet as those papers clarify, there's a excessive measure of overlap among the various ways. specifically, there's contract that the basic challenge is the avoidence of "overfitting"-Le., the place a version matches the given info very heavily, yet is a negative predictor for brand new information; in different phrases, the version has partially outfitted the "noise" within the unique data.

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See Bhansali & Downham (1977), Atkinson (1980, 1981) and Friedman (1991). Critics of such an argument are concerned that such ad hoc choice of A lacks theoretical justification. They argue that any finite Awould lead to an inconsistent criterion. Hence a penalty term that depends on the sample size should be used. Specifically, it has been suggested that A = An should satisfy An --+ 00 and An/n --+ O. See Bozdogan (1987), Nishii (1988) and Zhao et al. (1986a,b). The purpose of this article is to provide a unified approach to the assessment of criterion (1).

Similarly, all distributions q( z) whose marginal is q(y) can be viewed as a manifold. We call it the observed manifold. The E-step of EM algorithm can be viewed as projection onto the observed manifold, and the M-step as projection onto the model manifold. The minimum distance of the two manifolds measured by the K-L divergence is achieved by the estimated empirical distribution q( z) and the estimated true distribution p( z IfJ). 4 Criterion for Model Selection Let us consider criteria to choose good models from a set of candidate models {pi(zI8 i )} on the basis of the observed data q(y).

An information-theoretic approach to model selection, through minimum-bit data representation, is explored. Similarity of the asymptotic form of Rissanen's criterion, obtained from a minimum-bit data representation approach, to criteria derived from a Bayesian approach, is discussed. KEY WORDS: model selection, model evaluation, Akaike's information criterion, Ale, Schwarz's criterion, Kashyap's criterion, Bayesian inference, posterior probabilities. 4,1 Introduction; sets of alternative models Usually a statistical analysis involves consideration of a set of alternative models for the data.

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Selecting Models from Data: Artificial Intelligence and Statistics IV by D. J. Hand (auth.), P. Cheeseman, R. W. Oldford (eds.)


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