Selecting Models from Data: Artificial Intelligence and by D. J. Hand (auth.), P. Cheeseman, R. W. Oldford (eds.) 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 provided 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 facts to debate difficulties of mutual curiosity. The trade has broadened study in either fields and has strongly encourĀ­ elderly interdisciplinary paintings. The topic ofthe 1993 AI and records workshop used to be: "Selecting types from Data". The papers during this quantity attest to the variety of techniques to version choice and to the ubiquity of the matter. either data 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 different ways. specifically, there's contract that the elemental challenge is the avoidence of "overfitting"-Le., the place a version matches the given information very heavily, yet is a terrible predictor for brand new facts; in different phrases, the version has partially outfitted the "noise" within the unique data.

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Extra info for Selecting Models from Data: Artificial Intelligence and Statistics IV

Example text

PK = I, where the fe's are densities, usually from a specified parametric family. The choice of the number K of components in the mixture is a model-selection problem. It would be helpful if the above model-selection criteria could be applied. However, regularity conditions are required forthe validity of the expansions leading to these criteria. Unfortunately, these conditions are not met for the finite mixture model. The problem is that if Pe is set equal to zero, the parameters of the corresponding fe become meaningless.

In general, suppose that there are a number of possible models that can be used to describe the data. Let the candidate parametric models be denoted by f,,( x, 9k ), 9" EO", k = 1, ... ,K. The FPE criterion can be defined as where iJ" is the maximum likelihood estimate of 9" under the kth model and dim(0,,) is the dimension of the parameter space 0". Under regularity conditions, we expect that results similar to the linear regression case would hold for the general C*( k, ,\) criterion. In particular, the rule of thumb ,\ E [3,4] would still be valid.

77,657-658. [Linhart and Zucchini 1986) Linhart, H. and Zucchini, W. (1986) Model Selection. John Wiley & Sons, New York. [Parzen 1982) Parzen, E. (1982) "Maximum Entropy Interpretation of Autoregressive Spectral Densities," Statist. and Prob. Lttrs. 1, 7-11. [Rissanen 1978) Rissanen, J. (1978) "Modeling by Shortest Data Description," Automatica 14,465-471. [Rissanen 1985) Rissanen, J. (1985) "Minimum-Description-Length Principle," Ency. Statist. Sci. 5, 523-527. John Wiley & Sons, New York. [Rissanen 1986) Rissanen, J.

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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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