An Exponential Representation in the API Algorithm for Hidden Markov Models Training

  • Sébastien Aupetit
  • Nicolas Monmarché
  • Mohamed Slimane
  • Pierre Liardet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)


In this paper, we show how an efficient ant based algorithm, called API and initially designed to perform real parameter optimization, can be adapted to the difficult problem of Hidden Markov Models training. To this aim, a transformation of the search space that preserves API’s vectorial moves is introduced. Experiments are conducted with various temporal series extracted from images.


Search Space Hide Markov Model Hide State Quadratic Assignment Problem Model Hide Markov Model 
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 2006

Authors and Affiliations

  • Sébastien Aupetit
    • 1
  • Nicolas Monmarché
    • 1
  • Mohamed Slimane
    • 1
  • Pierre Liardet
    • 2
  1. 1.Laboratoire d’Informatique, Polytech’ToursUniversité François-Rabelais de ToursToursFrance
  2. 2.Laboratoire ATP, UMR-CNRS 6632Université de Provence, CMIMarseilleFrance

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