Learning Efficiency of Very Simple Grammars from Positive Data

  • Ryo Yoshinaka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4754)


The class of very simple grammars is known to be polynomial-time identifiable in the limit from positive data. This paper gives even more general discussion on the efficiency of identification of very simple grammars from positive data, which includes both positive and negative results. In particular, we present an alternative efficient inconsistent learning algorithm for very simple grammars.


Learning Algorithm Polynomial Time Target Language Target Class Positive Data 
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 2007

Authors and Affiliations

  • Ryo Yoshinaka
    • 1
  1. 1.INRIA-LorraineFrance

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