Inference Improvement by Enlarging the Training Set While Learning DFAs

  • Pedro García
  • José Ruiz
  • Antonio Cano
  • Gloria Alvarez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

A new version of the RPNI algorithm, called RPNI2, is presented. The main difference between them is the capability of the new one to extend the training set during the inference process. The effect of this new feature is specially notorious in the inference of languages generated from regular expressions and Non-deterministic Finite Automata (NFA). A first experimental comparison is done between RPNI2 and DeLeTe2, other algorithm that behaves well with the same sort of training data.

Keywords

Regular Expression Target Language Regular Language Inclusion Relation Grammatical Inference 
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 2005

Authors and Affiliations

  • Pedro García
    • 1
  • José Ruiz
    • 1
  • Antonio Cano
    • 1
  • Gloria Alvarez
    • 2
  1. 1.Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de ValenciaValenciaSpain
  2. 2.Seccional Cali, Grupo de Investigación DESTINOPontificia Universidad JaverianaCaliColombia

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