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Efficient Learning of k-Reversible Context-Free Grammars from Positive Structural Examples

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Book cover Grammatical Inference: Algorithms and Applications (ICGI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3264))

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Abstract

This paper discusses on the learnability of context free grammars(CFGs) within the framework of identification in the limit from positive structural examples. We will introduce a new subclass of CFGs, called a class of k-reversiblecontext-free grammars and propose an O(kdn 3) algorithm for learning this new subclass of CFGs, where d is the maximum of the ranks of given skeletal trees, and n is the total size of the given examples.

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References

  1. Angluin, D.: Inference of Reversible Languages. J. Assoc. Comput. Mach. 29, 741–765 (1982)

    MATH  MathSciNet  Google Scholar 

  2. Lopez, D., Sempere, J.M.P.: Garcia Inference of reversible tree languages. IEEE Transactions on Systems, Man and Cybernetics 34(4) (August 2004)

    Google Scholar 

  3. Gold, E.M.: Language identification in the limit. Inf. Control 10, 447–474 (1967)

    Article  MATH  Google Scholar 

  4. Fernau, H.: Learning tree languages from text. In: Kivinen, J., Sloan, R.H. (eds.) COLT 2002. LNCS (LNAI), vol. 2375, pp. 153–168. Springer, Heidelberg (2002)

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  5. Oates, T., Desai, D., Bhat, V.: Learning k-Reversible Context-Free Grammars from Positive Structural Examples. In: Proc. of Nineteenth International Conference on Machine Learning (2002)

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  6. Sakakibara, Y.: Efficient Learning of Context-Free Grammars from Positive Structural Examples. Information and Computation 97, 23–60 (1992)

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Electro-Communications, Chofugaoka 1-5-1, Chofu, Tokyo, 182-8585, Japan

    Shinnosuke Seki & Satoshi Kobayashi

Authors
  1. Shinnosuke Seki
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  2. Satoshi Kobayashi
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Editor information

Editors and Affiliations

  1. Institute of Informatics and Telecommunications, National Centre for Scientific Research “Demokritos”, Athens, Greece

    Georgios Paliouras

  2. Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, 223-8522, Yokohama, Japan

    Yasubumi Sakakibara

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© 2004 Springer-Verlag Berlin Heidelberg

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Seki, S., Kobayashi, S. (2004). Efficient Learning of k-Reversible Context-Free Grammars from Positive Structural Examples. In: Paliouras, G., Sakakibara, Y. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2004. Lecture Notes in Computer Science(), vol 3264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30195-0_30

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  • DOI: https://doi.org/10.1007/978-3-540-30195-0_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23410-4

  • Online ISBN: 978-3-540-30195-0

  • eBook Packages: Springer Book Archive

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