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Polynomial Time Algorithm for Inferring Subclasses of Parallel Internal Column Contextual Array Languages

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Combinatorial Image Analysis (IWCIA 2017)

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Abstract

In [2, 16] a new method of description of pictures of digitized rectangular arrays is introduced based on contextual grammars, called parallel internal contextual array grammars. In this paper, we pay our attention on parallel internal column contextual array grammars and observe that the languages generated by these grammars are not inferable from positive data only. We define two subclasses of parallel internal column contextual array languages, namely, k-uniform and strictly parallel internal column contextual languages which are incomparable and not disjoint classes and provide identification algorithms to learn these classes.

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Notes

  1. 1.

    In an 1-sided contextual rule either left context is \(\varLambda \) or right context is \(\varLambda \).

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Authors and Affiliations

  1. Information Technology, Alliance University, Bangalore, 562106, India

    Abhisek Midya, Saleem Malik & Shaleen Bhatnagar

  2. Department of Mathematics, Madras Christian College, Chennai, 600059, India

    D. G. Thomas

  3. Computer Science and Engineering, Christ University Faculty of Engineering, Bangalore, 560074, India

    Alok Kumar Pani

Authors
  1. Abhisek Midya
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  2. D. G. Thomas
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  3. Alok Kumar Pani
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  4. Saleem Malik
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  5. Shaleen Bhatnagar
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Correspondence to Abhisek Midya .

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Editors and Affiliations

  1. SUNY Buffalo State, Buffalo, New York, USA

    Valentin E. Brimkov

  2. Department of Applied Professional Studies, SUNY Fredonia, Fredonia, New York, USA

    Reneta P. Barneva

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Midya, A., Thomas, D.G., Pani, A.K., Malik, S., Bhatnagar, S. (2017). Polynomial Time Algorithm for Inferring Subclasses of Parallel Internal Column Contextual Array Languages. In: Brimkov, V., Barneva, R. (eds) Combinatorial Image Analysis. IWCIA 2017. Lecture Notes in Computer Science(), vol 10256. Springer, Cham. https://doi.org/10.1007/978-3-319-59108-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-59108-7_13

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