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Inducing Hidden Markov Models to Model Long-Term Dependencies

  • Jérôme Callut
  • Pierre Dupont
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3720)

Abstract

We propose in this paper a novel approach to the induction of the structure of Hidden Markov Models. The induced model is seen as a lumped process of a Markov chain. It is constructed to fit the dynamics of the target machine, that is to best approximate the stationary distribution and the mean first passage times observed in the sample. The induction relies on non-linear optimization and iterative state splitting from an initial order one Markov chain.

Keywords

HMM topology induction Partially observable Markov models Mean first passage times Lumped Markov process State splitting algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jérôme Callut
    • 1
  • Pierre Dupont
    • 1
  1. 1.Department of Computing Science and Engineering, INGIUniversité catholique de LouvainLouvain-la-NeuveBelgium

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