Second-Order Belief Hidden Markov Models
Artificial Intelligence
2015-01-23 v1
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
Cite
@article{arxiv.1501.05613,
title = {Second-Order Belief Hidden Markov Models},
author = {Jungyeul Park and Mouna Chebbah and Siwar Jendoubi and Arnaud Martin},
journal= {arXiv preprint arXiv:1501.05613},
year = {2015}
}