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Related Experiment Videos

Inference of reversible tree languages.

Damián López1, José M Sempere, Pedro García

  • 1Departamento de Sistemas Informáticos y Computación, Universidad Politécnica of Valencia, 46071 Valencia, Spain. dlopez@dsic.upv.es

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 7, 2004
PubMed
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This study introduces an efficient algorithm for learning k-testable tree languages. The research focuses on k-reversibility and k-testability in regular tree language inference.

Area of Science:

  • Formal Languages and Automata Theory
  • Computational Learning Theory

Background:

  • Regular tree languages are fundamental in computer science, with applications in parsing and pattern matching.
  • Understanding properties like k-testability is crucial for efficient language learning and analysis.

Purpose of the Study:

  • To investigate k-reversibility and k-testability within the context of regular tree languages.
  • To develop a polynomial-time algorithm for inferring k-testable tree languages.

Main Methods:

  • The study employs theoretical analysis of formal language properties.
  • An inference algorithm is presented for learning k-testable tree languages from samples.

Main Results:

  • A novel inference algorithm for k-testable tree languages is developed.

Related Experiment Videos

  • The algorithm demonstrates polynomial-time complexity relative to sample size.
  • The paper establishes relationships between k-testable tree languages and other known language classes.

    • Conclusions:

    • The proposed algorithm offers an efficient method for learning k-testable tree languages.
    • The findings contribute to the theoretical understanding of tree language learnability.
    • Further properties of these specialized tree languages are elucidated.