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Learning of recognizable picture languages

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Parallel Image Analysis (ICPIA 1992)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 654))

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Abstract

Learning of certain classes of two-dimensional picture languages is considered. Linear time algorithms that learn in the limit, from positive data the classes of local picture languages and locally testable picture languages are presented. A crucial step for obtaining the learning algorithm for local picture languages is an explicit construction of a two-dimensional on-line tessellation acceptor for a given local picture language. An efficient algorithm that learns the class of recognizable picture languages from positve data and restricted subset queries, is presented in contrast to the fact that this class is not learnable in the limit from positive data alone.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Madras Christian College, 600 059, Tambaram, Madras, India

    Rani Siromoney, Lisa Mathew, K. G. Subramanian & V. Rajkumar Dare

Authors
  1. Rani Siromoney
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  2. Lisa Mathew
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  3. K. G. Subramanian
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  4. V. Rajkumar Dare
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Editor information

Akira Nakamura Maurice Nivat Ahmed Saoudi Patrick S. P. Wang Katsushi Inoue

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© 1992 Springer-Verlag Berlin Heidelberg

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Siromoney, R., Mathew, L., Subramanian, K.G., Dare, V.R. (1992). Learning of recognizable picture languages. In: Nakamura, A., Nivat, M., Saoudi, A., Wang, P.S.P., Inoue, K. (eds) Parallel Image Analysis. ICPIA 1992. Lecture Notes in Computer Science, vol 654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56346-6_43

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  • DOI: https://doi.org/10.1007/3-540-56346-6_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56346-4

  • Online ISBN: 978-3-540-47538-5

  • eBook Packages: Springer Book Archive

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