Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences - Archive ouverte HAL Access content directly
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Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences

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Abstract

In this study, we propose a method called sequence breaking framework to search high local maximum of the likelihood by providing starting values based on the observations for the Expectation-Maximization algorithm, for Hidden semi-Markov model parameter estimation. The method is shown to be efficient on several datasets with multiple categorical sequences.
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Dates and versions

hal-02129122 , version 1 (14-05-2019)

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  • HAL Id : hal-02129122 , version 1

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Brice Olivier, Anne Guérin-Dugué, Jean-Baptiste Durand. Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences. JdS 2019 - 51èmes Journées de Statistique, Jun 2019, Vandœuvre-lès-Nancy, France. pp.1-7. ⟨hal-02129122⟩
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