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The Computational Complexity of Occam’s Razor

  • Conference paper
GWAI-90 14th German Workshop on Artificial Intelligence

Part of the book series: Informatik-Fachberichte ((INFORMATIK,volume 251))

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Abstract

This paper is concerned with the computational feasibility of finding a simple theory for explaining the observed data. In particular, learning to classify multiple classes by a minimal set of discriminating attributes is investigated. The paper presents an NP-hardness proof for that learning task. Furthermore, a fast algorithm for approximating the simplest theory is given.

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References

  1. A. Blumer, A. Ehrenfeucht, D. Haussler, M. Warmuth: Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension; Proceedings, 18th Symp. on Theory of Computing 1986.

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  2. A. Blumer, A. Ehrenfeucht, D. Haussler, M. Warmuth: Occam’s Razor; Information Processing Letters 24 (1987) pp. 377–380; North-Holland

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  4. D.S. Johnson: Approximation Algorithms for Combinatorial Problems; Journal for Computer and Systems Sciences, 9, pp. 256–278, 1974

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  5. Nelson Goodman: Fact, Fiction and Forecast, London, 1954

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Author information

Authors and Affiliations

  1. Institut für Angewandte Informatik, Technische Universität Berlin, Franklinstr.28/29, D-1000, Berlin 10, West Germany

    Achim G. Hoffmann

Authors
  1. Achim G. Hoffmann
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Editor information

Editors and Affiliations

  1. >c/o Danet GmbH, Pallaswiesenstr. 203, D-6100, Darmstadt, Deutschland

    Heinz Marburger

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© 1990 Springer-Verlag Berlin Heidelberg

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Hoffmann, A.G. (1990). The Computational Complexity of Occam’s Razor. In: Marburger, H. (eds) GWAI-90 14th German Workshop on Artificial Intelligence. Informatik-Fachberichte, vol 251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76071-6_31

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  • DOI: https://doi.org/10.1007/978-3-642-76071-6_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-53132-6

  • Online ISBN: 978-3-642-76071-6

  • eBook Packages: Springer Book Archive

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