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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F06%3A03120561%21RIV07-MSM-21230___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
The paper presents new application of Particle Swarm Optimization for training Hidden Markov Models. The approach is verified on artificial data and further, the application to Intracranial Pressure (ICP) analysis is described. In comparison with Expectation Maximization algorithm, commonly used for the HMM training problem, the PSO approach is less sensitive on sticking to local optima because of its global character. However this advantage depends on character of the particular problem. The IC analysis is the case of such problem where it is suitable to use the PSO strategy. This is demonstrated by better classification result (85.1%) in comparison with the EM algorithm (76.3%). Není k dispozici The paper presents new application of Particle Swarm Optimization for training Hidden Markov Models. The approach is verified on artificial data and further, the application to Intracranial Pressure (ICP) analysis is described. In comparison with Expectation Maximization algorithm, commonly used for the HMM training problem, the PSO approach is less sensitive on sticking to local optima because of its global character. However this advantage depends on character of the particular problem. The IC analysis is the case of such problem where it is suitable to use the PSO strategy. This is demonstrated by better classification result (85.1%) in comparison with the EM algorithm (76.3%).
dcterms:title
Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis Není k dispozici Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis
skos:prefLabel
Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis Particle Swarm Optimization for Hidden Markov Models with Application to Intracranial Pressure Analysis Není k dispozici
skos:notation
RIV/68407700:21230/06:03120561!RIV07-MSM-21230___
n3:strany
175 ; 177
n3:aktivita
n20:Z
n3:aktivity
Z(MSM6840770012)
n3:dodaniDat
n6:2007
n3:domaciTvurceVysledku
n15:6579191 n15:9431446 n15:2793172
n3:druhVysledku
n11:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
491572
n3:idVysledku
RIV/68407700:21230/06:03120561
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Evolution; Expectation Maximization; Hidden Markov Models; Intracranial pressure; Nature-inspired; Particle Swarm Optimization
n3:klicoveSlovo
n4:Evolution n4:Intracranial%20pressure n4:Particle%20Swarm%20Optimization n4:Nature-inspired n4:Hidden%20Markov%20Models n4:Expectation%20Maximization
n3:kontrolniKodProRIV
[33FD89920AE1]
n3:mistoKonaniAkce
Brno
n3:mistoVydani
Brno
n3:nazevZdroje
Analysis of Biomedical Signals and Images - Proceedings of Biosignal 2006
n3:obor
n21:JC
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n6:2006
n3:tvurceVysledku
Macaš, Martin Novák, Daniel Lhotská, Lenka
n3:typAkce
n14:EUR
n3:zahajeniAkce
2006-06-28+02:00
n3:zamer
n17:MSM6840770012
s:numberOfPages
3
n8:hasPublisher
VUTIUM Press
n5:isbn
80-214-3152-0
n18:organizacniJednotka
21230