Hidden Markov Models Training by a Particle Swarm Optimization Algorithm

  • Sébastien Aupetit
  • Nicolas Monmarché
  • Mohamed Slimane


In this work we consider the problem of Hidden Markov Models (HMM) training. This problem can be considered as a global optimization problem and we focus our study on the Particle Swarm Optimization (PSO) algorithm. To take advantage of the search strategy adopted by PSO, we need to modify the HMM's search space. Moreover, we introduce a local search technique from the field of HMMs and that is known as the Baum–Welch algorithm. A parameter study is then presented to evaluate the importance of several parameters of PSO on artificial data and natural data extracted from images.

Mathematics Subject Classifications (2000):

68T05 [68Q32, 91E40] 68C35 [82C80] 90C59 

Key words

particle swarm optimization hidden markov models training image learning 


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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Sébastien Aupetit
    • 1
  • Nicolas Monmarché
    • 1
  • Mohamed Slimane
    • 1
  1. 1.Laboratoire d'Informatique, Polytech'ToursUniversité François-Rabelais de ToursToursFrance

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