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Learning Classes of Probabilistic Automata

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Learning Theory (COLT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3120))

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Abstract

Probabilistic finite automata (PFA) model stochastic languages, i.e. probability distributions over strings. Inferring PFA from stochastic data is an open field of research. We show that PFA are identifiable in the limit with probability one. Multiplicity automata (MA) is another device to represent stochastic languages. We show that a MA may generate a stochastic language that cannot be generated by a PFA, but we show also that it is undecidable whether a MA generates a stochastic language. Finally, we propose a learning algorithm for a subclass of PFA, called PRFA.

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Authors and Affiliations

  1. LIF-CMI, 39, rue F. Joliot Curie, 13453, Marseille Cedex 13, France

    François Denis & Yann Esposito

Authors
  1. François Denis
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  2. Yann Esposito
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Editor information

Editors and Affiliations

  1. The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, Gower St., WC1E 6BT, London

    John Shawe-Taylor

  2. Google, 1600 Amphitheater Parkway, CA 94043, Mountain View, USA

    Yoram Singer

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© 2004 Springer-Verlag Berlin Heidelberg

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Denis, F., Esposito, Y. (2004). Learning Classes of Probabilistic Automata. In: Shawe-Taylor, J., Singer, Y. (eds) Learning Theory. COLT 2004. Lecture Notes in Computer Science(), vol 3120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27819-1_9

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  • DOI: https://doi.org/10.1007/978-3-540-27819-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22282-8

  • Online ISBN: 978-3-540-27819-1

  • eBook Packages: Springer Book Archive

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