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Probabilistic finite-state machines--part II.

Enrique Vidal1, Frank Thollard, Colin de la Higuera

  • 1Departamento de Sistemas Informáticos y Computación and Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Camino de Vera s/n, E-46071 Valencia, Spain. evidal@iti.upv.es

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 15, 2005
PubMed
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This study explores probabilistic finite-state automata, comparing them to hidden Markov models and n-grams. It offers new theorems and algorithms for pattern recognition and string generation.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Probabilistic finite-state machines are integral to pattern recognition and related fields.
  • Part I of this research provided a survey and property analysis of these machines.

Purpose of the Study:

  • To investigate the relationships between probabilistic finite-state automata and other string-generating models.
  • To present current advancements in theorems, algorithms, and properties for these automata.

Main Methods:

  • Comparative analysis of probabilistic finite-state automata with hidden Markov models and n-grams.
  • Development of new theoretical frameworks and algorithms.

Main Results:

  • Established key relationships and distinctions between probabilistic finite-state automata and comparable models.

Related Experiment Videos

  • Provided a comprehensive overview of the state-of-the-art in probabilistic finite-state automata research.

    • Conclusions:

    • The findings enhance understanding of probabilistic finite-state automata within the broader context of machine learning and pattern recognition.
    • This work offers valuable theoretical and algorithmic resources for researchers and practitioners in the field.