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Second-Order Belief Hidden Markov Models

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.
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Contributor : Arnaud Martin <>
Submitted on : Thursday, January 22, 2015 - 1:45:24 PM
Last modification on : Friday, March 6, 2020 - 4:10:03 PM
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Jungyeul Park, Mouna Chebbah, Siwar Jendoubi, Arnaud Martin. Second-Order Belief Hidden Markov Models. Belief 2014, Sep 2014, Oxford, United Kingdom. pp.284 - 293, ⟨10.1007/978-3-319-11191-9_31⟩. ⟨hal-01108238⟩



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