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Inferring Unions of the Pattern Languages by the Most Fitting Covers

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Algorithmic Learning Theory (ALT 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3734))

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Abstract

We are interested in learning unions of the pattern languages in the limit from positive data using strategies that guarantee some form of minimality during the learning process. It is known that for any class of languages with so-called finite elasticity, any learning strategy that finds a language that is minimal with respect to inclusion (MINL) ensures identification in the limit. We consider a learning strategy via another form of minimality — the minimality in the number of elements that are shorter than a specified length. A search for languages with this minimality is possible in many cases, and the search can be adapted to identify any class where every language in the class has a characteristic set within the class. We compare solutions using this strategy to those from MINL to illustrate how we may obtain solutions that fulfill both notions of minimality. Finally, we show how the results are relevant using some subclasses of the pattern languages.

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Author information

Authors and Affiliations

  1. Graduate School of Computer Science and Systems, Kyushu Institute of Technology, Iizuka, 820, Japan

    Yen Kaow Ng

  2. Department of Artificial Intelligence, Kyushu Institute of Technology, Iizuka, 820, Japan

    Takeshi Shinohara

Authors
  1. Yen Kaow Ng
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  2. Takeshi Shinohara
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Editor information

Editors and Affiliations

  1. School of Computing, National University of Singapore, 117590, Singapore

    Sanjay Jain

  2. Ruhr-Universität Bochum, Germany

    Hans Ulrich Simon

  3. Department of Information and Communication Engineering, Faculty of Electro-Communications, The University of Electro-Communications, Chofugaoka 1–5–1, Chofu, 182-8585, Tokyo, Japan

    Etsuji Tomita

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© 2005 Springer-Verlag Berlin Heidelberg

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Ng, Y.K., Shinohara, T. (2005). Inferring Unions of the Pattern Languages by the Most Fitting Covers. In: Jain, S., Simon, H.U., Tomita, E. (eds) Algorithmic Learning Theory. ALT 2005. Lecture Notes in Computer Science(), vol 3734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564089_22

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  • DOI: https://doi.org/10.1007/11564089_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29242-5

  • Online ISBN: 978-3-540-31696-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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