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From Computational Learning Theory to Discovery Science

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Automata, Languages and Programming

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1644))

Abstract

Machine learning has been one of the important subjects of AI that is motivated by many real world applications. In theoretical computer science, researchers also have introduced mathematical frameworks for investigating machine learning, and in these frameworks, many interesting results have been obtained. Now we are proceeding to a new stage to study how to apply these fruitful theoretical results to real problems. We point out in this paper that “adaptivity” is one of the important issues when we consider applications of learning techniques, and we propose one learning algorithm with this feature.

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Author information

Authors and Affiliations

  1. Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo, 152-8552, Japan

    Osamu Watanabe

Authors
  1. Osamu Watanabe
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Editor information

Editors and Affiliations

  1. Institute for Computer Science, Academy of Sciences of the Czech Republic, Pod vodarenskou vezi 2, 182 07, Prague 8 - Liben, Czech Republic

    Jirí Wiedermann

  2. ILLC-WINS-University of Amsterdam, Plantage Muidergracht 24, 1018, TV, Amsterdam, The Netherlands

    Peter van Emde Boas

  3. BRICS, Department of Computer Science, University of Aarhus, Ny Munkegade, Bldg. 540, DK, 8000, Aarhus C, Denmark

    Mogens Nielsen

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© 1999 Springer-Verlag Berlin Heidelberg

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Watanabe, O. (1999). From Computational Learning Theory to Discovery Science. In: Wiedermann, J., van Emde Boas, P., Nielsen, M. (eds) Automata, Languages and Programming. Lecture Notes in Computer Science, vol 1644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48523-6_11

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  • DOI: https://doi.org/10.1007/3-540-48523-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66224-2

  • Online ISBN: 978-3-540-48523-0

  • eBook Packages: Springer Book Archive

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