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International Workshop on Algorithmic Learning Theory

ALT 1995: Algorithmic Learning Theory pp 41–54Cite as

Learning strongly deterministic even linear languages from positive examples

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 997))

Abstract

We consider the problem of learning deterministic even linear languages from positive examples. By a “deterministic” even linear language we mean a language generated by an LR(k) even linear grammar. We introduce a natural subclass of LR(k) even linear languages, called LR(k) in the strong sense, and show that this subclass is learnable in the limit from positive examples. Furthermore, we propose a learning algorithm that identifies this subclass in the limit with almost linear time in updating conjectures. As a corollary, in terms of even linear grammars, we have a learning algorithm for k-reversible languages that is more efficient than the one proposed by Angluin[Ang82].

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Author information

Authors and Affiliations

  1. First Research Lab., Institute for Social Information Science, Fujitsu Laboratories Ltd., 140 Miyamoto, Numazushi, 410-03, Shizuoka, Japan

    Takeshi Koshiba

  2. Department of Computer Science, University of Tampere, P.O.Box 607, SF-33101, Tampere, Finland

    Erkki Mäkinen

  3. Second Research Lab., Institute for Social Information Science, Fujitsu Laboratories Ltd., 1-9-3 Nakase, Mihama-ku, Chiba-shi, 261, Chiba, Japan

    Yuji Takada

Authors
  1. Takeshi Koshiba
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  2. Erkki Mäkinen
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  3. Yuji Takada
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Editor information

Klaus P. Jantke Takeshi Shinohara Thomas Zeugmann

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© 1995 Springer-Verlag Berlin Heidelberg

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Koshiba, T., Mäkinen, E., Takada, Y. (1995). Learning strongly deterministic even linear languages from positive examples. In: Jantke, K.P., Shinohara, T., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 1995. Lecture Notes in Computer Science, vol 997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60454-5_27

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  • DOI: https://doi.org/10.1007/3-540-60454-5_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60454-9

  • Online ISBN: 978-3-540-47470-8

  • eBook Packages: Springer Book Archive

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