Hidden Markov models training by a particle swarm optimization algorithm

Hidden Markov models training by a particle swarm optimization algorithm

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Article ID: iaor20082740
Country: United Kingdom
Volume: 6
Issue: 2
Start Page Number: 175
End Page Number: 193
Publication Date: Jun 2007
Journal: Journal of Mathematical Modelling and Algorithms
Authors: , ,
Keywords: learning, markov processes, optimization
Abstract:

In this work we consider the problem of Hidden Markov Models (HMM) training. This problem can be considered as a global optimization problem and we focus our study on the Particle Swarm Optimization (PSO) algorithm. To take advantage of the search strategy adopted by PSO, we need to modify the HMM’s search space. Moreover, we introduce a local search technique from the field of HMMs and that is known as the Baum–Welch algorithm. A parameter study is then presented to evaluate the importance of several parameters of PSO on artificial data and natural data extracted from images.

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