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PAC Learning of Some Subclasses of Context-Free Grammars with Basic Distributional Properties from Positive Data

  • Chihiro Shibata
  • Ryo Yoshinaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8139)

Abstract

In recent years different interesting subclasses of cfls have been found to be learnable by techniques generically called distributional learning. The theoretical study on the exact learning of cfls by those techniques under different learning scheme is now quite mature. On the other hand, positive results on the pac learnability of cfls are rather limited and quite weak. This paper shows that several subclasses of context-free languages that are known to be exactly learnable with membership queries by distributional learning techniques are pac learnable from positive data under some assumptions on the string distribution.

Keywords

Regular Language Positive Data Probabilistic Learning Membership Query Nonterminal Symbol 
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 2013

Authors and Affiliations

  • Chihiro Shibata
    • 1
  • Ryo Yoshinaka
    • 2
  1. 1.School of Computer ScienceTokyo University of TechnologyJapan
  2. 2.Graduate School of InformaticsKyoto UniversityJapan

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