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Inferring Deterministic Linear Languages

  • Colin de la Higuera
  • Jose Oncina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2375)

Abstract

Linearity and determinism seem to be two essential conditions for polynomial language learning to be possible. We compare several definitions of deterministic linear grammars, and for a reasonable definition prove the existence of a canonical normal form. This enables us to obtain positive learning results in case of polynomial learning from a given set of both positive and negative examples. The resulting class is the largest one for which this type of results has been obtained so far.

Keywords

Polynomial Time Regular Language Terminal Symbol Grammatical Inference Regular Grammar 
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 2002

Authors and Affiliations

  • Colin de la Higuera
    • 1
  • Jose Oncina
    • 2
  1. 1.EURISEUniversité de Saint-EtienneSaint-EtienneFrance
  2. 2.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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