Hidden Markov Models Training Using Population-based Metaheuristics

DOI10.1007/978-3-540-72960-0_20zbMath1211.90272OpenAlexW1533497953MaRDI QIDQ3509450

Sébastien Aupetit, Nicolas Monmarché, Mohamed Slimane

Publication date: 1 July 2008

Published in: Natural Computing Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-72960-0_20

zbMATH Keywords

hidden Markov modeltabu searchgenetic algorithmparticle swarm optimizationsearch space

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Markov and semi-Markov decision processes (90C40)

Cites Work

• Optimization by Simulated Annealing
• A low complexity simulated annealing approach for training hidden Markov models
• The hierarchical hidden Markov model: Analysis and applications
• Arbres de Markov triplet et fusion de Dempster-Shafer. (Triplet Markov trees and Dempster-Shafer fusion)
• Future paths for integer programming and links to artificial intelligence
• Recursive computation of smoothed functionals of hidden Markovian processes using a particle approximation
• The segmental K-means algorithm for estimating parameters of hidden Markov models
• Tabu Search—Part I
• Tabu Search—Part II
• The double chain markov model
• Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
• An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
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