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Regular grammatical inference from positive and negative samples by genetic search: the GIG method

  • Pierre Dupont
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 862)

Abstract

We recall briefly in this paper the formal theory of regular grammatical inference from positive and negative samples of the language to be learned. We state this problem as a search toward an optimal element in a boolean lattice built from the positive information. We explain how a genetic search technique may be applied to this problem and we introduce a new set of genetic operators. In view of limiting the increasing complexity as the sample size grows, we propose a semi-incremental procedure. Finally, an experimental protocol to assess the performance of a regular inference technique is detailed and comparative results are given.

Keywords

Negative Sample Regular Language Finite Automaton Optimal Partition Correct Classification Rate 
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 1994

Authors and Affiliations

  • Pierre Dupont
    • 1
  1. 1.France Télécom CNET/LAA/TSS/RCPLannion CedexFrance

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