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Minimal model complexity search

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Progress in Artificial Intelligence (EPIA 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 990))

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Abstract

SURFER is an empirical discovery system that given a set of input data and a modelling vocabulary returns the model that best describes that data. The best model is considered to be the one that minimizes the description length of that model plus the data encoded using that model. The search for models is controlled by the a priori estimate of model likelihoods as encoded in the modelling vocabulary. SURFER includes domain independent mechanisms for identifying redundant models and for finding free parameters. The system is described together with the results of running the system on several different types of problems.

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Author information

Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, 15213-3891, Pittsburgh, PA, USA

    Chris McConnell

Authors
  1. Chris McConnell
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Editor information

Carlos Pinto-Ferreira Nuno J. Mamede

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© 1995 Springer-Verlag Berlin Heidelberg

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McConnell, C. (1995). Minimal model complexity search. In: Pinto-Ferreira, C., Mamede, N.J. (eds) Progress in Artificial Intelligence. EPIA 1995. Lecture Notes in Computer Science, vol 990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60428-6_15

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  • DOI: https://doi.org/10.1007/3-540-60428-6_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60428-0

  • Online ISBN: 978-3-540-45595-0

  • eBook Packages: Springer Book Archive

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