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Probabilistic finite-state machines--part I.

Enrique Vidal1, Franck 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
Summary

This paper surveys probabilistic finite-state machines, essential for pattern recognition tasks like machine learning and computational linguistics. It explores their definitions, properties, and connections to related generative models.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Probabilistic finite-state machines (PFSMs) are foundational in pattern recognition.
  • They are applied across diverse fields including machine learning, computational biology, and natural language processing.
  • Understanding PFSMs is crucial for advancing these interdisciplinary areas.

Purpose of the Study:

  • To provide a comprehensive survey of PFSMs, detailing their definitions and inherent properties.
  • To explore the relationships between PFSMs and other string-generating models such as hidden Markov models (HMMs) and n-grams.
  • To present current state-of-the-art theorems, algorithms, and properties related to these generative objects.

Main Methods:

  • Literature review and theoretical analysis of PFSMs.

Related Experiment Videos

  • Comparative study of PFSMs against HMMs and n-grams.
  • Development and presentation of novel theorems and algorithms.

    • Main Results:

    • A detailed exposition of PFSM definitions and properties.
    • Established connections and distinctions between PFSMs, HMMs, and n-grams.
    • A collection of theorems and algorithms representing the current state-of-the-art.

    Conclusions:

    • PFSMs are versatile tools with broad applications in pattern recognition and related fields.
    • The study clarifies the theoretical landscape of PFSMs and their relationship with other generative models.
    • This work serves as a foundational reference for researchers in machine learning, computational linguistics, and beyond.