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Learning under uniform distribution

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Fundamentals of Computation Theory (FCT 1989)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 380))

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Abstract

We study the learnability from examples of boolean formulae assuming that the examples satisfy a uniform distribution assumption. We analyze the requirements of known algorithms (upper and lower bounds) under uniform distribution and we propose a new combinatorial measure in order to characterize the complexity of boolean formulae.

Work partially supported by Italian project MPI ‘Algoritmi e Structure di Calcolo’.

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Author information

Authors and Affiliations

  1. Dept. of Mathematics, University of L'Aquila, L'Aquila, Italy

    A. Marchetti-Spaccamela

  2. Dept. of Mathematics, University of Roma "Tor Vergata", Roma, Italy

    M. Protasi

Authors
  1. A. Marchetti-Spaccamela
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  2. M. Protasi
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Editor information

J. Csirik J. Demetrovics F. Gécseg

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© 1989 Springer-Verlag Berlin Heidelberg

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Marchetti-Spaccamela, A., Protasi, M. (1989). Learning under uniform distribution. In: Csirik, J., Demetrovics, J., Gécseg, F. (eds) Fundamentals of Computation Theory. FCT 1989. Lecture Notes in Computer Science, vol 380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-51498-8_32

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  • DOI: https://doi.org/10.1007/3-540-51498-8_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-51498-5

  • Online ISBN: 978-3-540-48180-5

  • eBook Packages: Springer Book Archive

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