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Learning Classes of Probabilistic Automata

  • François Denis
  • Yann Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3120)

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.

Keywords

Convex Generator Learn Class Membership Query Complete Presentation Return Transition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • François Denis
    • 1
  • Yann Esposito
    • 1
  1. 1.LIF-CMIMarseille Cedex 13France

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