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Sequences Classification by Least General Generalisations

  • Frédéric Tantini
  • Alain Terlutte
  • Fabien Torre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6339)

Abstract

In this paper, we present a general framework for supervised classification. This framework provides methods like boosting and only needs the definition of a generalisation operator called lgg. For sequence classification tasks, lgg is a learner that only uses positive examples. We show that grammatical inference has already defined such learners for automata classes like reversible automata or k-TSS automata. Then we propose a generalisation algorithm for the class of balls of words. Finally, we show through experiments that our method efficiently resolves sequence classification tasks.

Keywords

sequence classification least general automata balls of words 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Frédéric Tantini
    • 1
  • Alain Terlutte
    • 2
  • Fabien Torre
    • 2
  1. 1.Parole, CNRS/LORIA Nancy 
  2. 2.Mostrare (INRIA Lille Nord Europe et CNRS LIFL)Université Lille Nord deFrance

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