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13

ECML
2005
Springer

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Inducing Hidden Markov Models to Model Long-Term Dependencies

13 years 10 months ago

Inducing Hidden Markov Models to Model Long-Term Dependencies

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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.

Jérôme Callut, Pierre Dupont

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ECML 2005 | Hidden Markov Models | Markov Chain | Mean ﬁrst Passage |

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Added	27 Jun 2010
Updated	27 Jun 2010
Type	Conference
Year	2005
Where	ECML
Authors	Jérôme Callut, Pierre Dupont

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