Second-Order Belief Hidden Markov Models
This is an incremental extension for pattern recognition researchers using belief functions.
The authors tackled the problem of extending belief function-based Hidden Markov Models from first-order to second-order, resulting in a new model that generalizes previous work.
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.