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Learning Stochastic Finite Automata

  • Colin de la Higuera
  • Jose Oncina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3264)

Abstract

Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of these finite state machines. In the setting of identification in the limit with probability one, we prove that stochastic deterministic finite automata cannot be identified from only a polynomial quantity of data. If concerned with approximation results, they become Pac-learnable if the L  ∞  norm is used. We also investigate queries that are sufficient for the class to be learnable.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Colin de la Higuera
    • 1
  • Jose Oncina
    • 2
  1. 1.EURISEUniversité de Saint-EtienneSaint-EtienneFrance
  2. 2.Departamento de Lenguajes y Sistemas InformaticosUniversidad de AlicanteAlicanteSpain

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