Constraints in Particle Swarm Optimization of Hidden Markov Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F06%3A03120667" target="_blank" >RIV/68407700:21230/06:03120667 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Constraints in Particle Swarm Optimization of Hidden Markov Models
Original language description
This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training Hidden Markov Models (HMMs). The problem of finding an optimal set of model parameters is numerical optimization problem constrained by stochastic character of HMM parameters. Constraint handling is carried out using three different ways and the results are compared to Baum-Welch algorithm (BW), commonly used for HMM training. The global searching PSO method is much less sensitive to local extremes and findsbetter solutions than the local BW algorithm, which often converges to local optima. The advantage of PSO approach was markedly evident, when longer training sequence was used.
Czech name
Není k dispozici
Czech description
Není k dispozici
Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2006
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Intelligent Data Engineering and Automated Learning - Proceedings of IDEAL 2006
ISBN
3-540-45485-3
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1399-1406
Publisher name
Springer
Place of publication
Berlin
Event location
Burgos
Event date
Sep 20, 2006
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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