Locality, Reversibility, and Beyond: Learning Languages from Positive Data

  • Tom Head
  • Satoshi Kobayashi
  • Takashi Yokomori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1501)


In algorithmic learning theory fundamental roles are played by the family of languages that are locally testable in the strict sense and by the family of reversible languages. These two families are shown to be the first two members of an infinite sequence of families of regular languages the members of which are learnable in the limit from positive data only. A uniform procedure is given for deciding, for each regular language R and each of our specified families, whether R belongs to the family. The approximation of arbitrary regular languages by languages belonging to these families is discussed. Further, we will give a uniform scheme for learning these families from positive data. Several research problems are also suggested.


reversible languages local languages regular languages identification in the limit from positive data approximate learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Tom Head
    • 1
  • Satoshi Kobayashi
    • 2
  • Takashi Yokomori
    • 3
  1. 1.Department of MathematicsBinghamton UniversityBinghamtonUSA
  2. 2.Department of Information ScienceTokyo Denki UniversitySaitamaJapan
  3. 3.Department of Mathematics, School of EducationWaseda UniversityTokyoJapan

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