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Learning strongly deterministic even linear languages from positive examples

  • Takeshi Koshiba
  • Erkki Mäkinen
  • Yuji Takada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 997)

Abstract

We consider the problem of learning deterministic even linear languages from positive examples. By a “deterministic” even linear language we mean a language generated by an LR(k) even linear grammar. We introduce a natural subclass of LR(k) even linear languages, called LR(k) in the strong sense, and show that this subclass is learnable in the limit from positive examples. Furthermore, we propose a learning algorithm that identifies this subclass in the limit with almost linear time in updating conjectures. As a corollary, in terms of even linear grammars, we have a learning algorithm for k-reversible languages that is more efficient than the one proposed by Angluin[Ang82].

Keywords

Learning Algorithm Nonnegative Integer Regular Language Finite Automaton Input String 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Takeshi Koshiba
    • 1
  • Erkki Mäkinen
    • 2
  • Yuji Takada
    • 3
  1. 1.First Research Lab., Institute for Social Information ScienceFujitsu Laboratories Ltd.ShizuokaJapan
  2. 2.Department of Computer ScienceUniversity of TampereTampereFinland
  3. 3.Second Research Lab., Institute for Social Information ScienceFujitsu Laboratories Ltd.ChibaJapan

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