Probabilistic Finite-State Machines-Part I

  • Authors:
  • Enrique Vidal;Franck Thollard;Colin de la Higuera;Francisco Casacuberta;Rafael C. Carrasco

  • Affiliations:
  • IEEE Computer Society;-;-;IEEE Computer Society;-

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2005

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Abstract

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n\hbox{-}{\rm{grams}} and provide theorems, algorithms, and properties that represent a current state of the art of these objects.