International Conference on Belief Functions

BELIEF 2014: Belief Functions: Theory and Applications pp 284-293 | Cite as

Second-Order Belief Hidden Markov Models

  • Jungyeul Park
  • Mouna Chebbah
  • Siwar Jendoubi
  • Arnaud Martin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8764)

Abstract

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model.

Keywords

Belief functions Dempster-Shafer theory first-order belief HMM second-order belief HMM probabilistic HMM 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jungyeul Park
    • 1
  • Mouna Chebbah
    • 1
    • 2
  • Siwar Jendoubi
    • 1
    • 2
  • Arnaud Martin
    • 1
  1. 1.UMR 6074 IRISAUniversité de Rennes1LannionFrance
  2. 2.LARODEC LaboratoryUniversity of Tunis,ISG TunisTunisia

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