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A beam search algorithm for PFSA inference

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Abstract

In the past, many methods have been proposed for the inference of probabilistic and non-probabilistic finite state automata from positive examples of their behaviour. In this paper, we introduce a search method guided by the information-theoretic Minimum Message Length principle to infer Probabilistic Finite State Automata (PFSA).1 The method is a beam search technique that searches for the best PFSA that accounts for a given dataset. Results of testing this method against some earlier algorithms are presented. A simulated annealing version of the beam search algorithm is also described as ongoing research in the area.

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Author notes

  1. A. Raman

    Present address: Department of Cognitive Science, Johns Hopkins University, 21218, Baltimore, MD, USA

Authors and Affiliations

  1. Computer Science Group, Institute of Information Sciences and Technology, Massey University, Private bag 11222, Palmerston North, New Zealand

    A. Raman

  2. Department of Computer Science, Victoria University, Wellington, New Zealand

    P. Andreae

  3. Basser Department of Computer Science, University of Sydney, Sydney, Australia

    J. Patrick

Authors
  1. A. Raman
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  2. P. Andreae
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  3. J. Patrick
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Corresponding author

Correspondence to A. Raman.

Additional information

The abbreviation PFSA is used to denote both the singular and plural of these automata

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Raman, A., Andreae, P. & Patrick, J. A beam search algorithm for PFSA inference. Pattern Analysis & Applic 1, 121–129 (1998). https://doi.org/10.1007/BF01237940

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  • DOI: https://doi.org/10.1007/BF01237940

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