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International Colloquium on Grammatical Inference

ICGI 2000: Grammatical Inference: Algorithms and Applications pp 157–170Cite as

Iterated Transductions and Efficient Learning from Positive Data: A Unifying View

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1891))

Abstract

In this paper, we will not focus on a learnability result of some specific language class, but on giving a set of language classes each of which is efficiently learnable in the limit from positive data. Furthermore, the set contains the class of k-reversible languages and the class of k-locally testable languages in the strict sense just as example language classes. This paper also proposes a framework for defining language classes based on iterated transductions. We believe that the framework is quite adequate for theoretically investigate the classes of languages which are efficiently learnable from positive data.

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Author information

Authors and Affiliations

  1. Dept. of Information Sciences, Tokyo Denki Univ., Ishizaka, Hatoyama-machi, Hiki-gun, Saitama, 350-0394, Japan

    Satoshi Kobayashi

Authors
  1. Satoshi Kobayashi
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Editor information

Editors and Affiliations

  1. INESC-ID/IST, R. Alves Redol 9, 1000, Lisboa, Portugal

    Arlindo L. Oliveira

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© 2000 Springer-Verlag Berlin Heidelberg

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Kobayashi, S. (2000). Iterated Transductions and Efficient Learning from Positive Data: A Unifying View. In: Oliveira, A.L. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2000. Lecture Notes in Computer Science(), vol 1891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45257-7_13

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  • DOI: https://doi.org/10.1007/978-3-540-45257-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41011-9

  • Online ISBN: 978-3-540-45257-7

  • eBook Packages: Springer Book Archive

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