Inducing Hidden Markov Models to model long-term dependencies

Callut, J.;Dupont, Pierre
(2005) 16th European Conference on Machine Learning (ECML)/9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) — Location: Oporto(Portugal) (3.October.2005)

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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.
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Callut, J., & Dupont, P. (2005). Inducing Hidden Markov Models to model long-term dependencies. Lecture Notes in Computer Science, 3720, 513-521. https://doi.org/10.1007/11564096_49 (Original work published 2005)