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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F07%3A03132430%21RIV08-MSM-21230___
rdf:type
skos:Concept n21:Vysledek
dcterms:description
Článek popisuje aplikace několika verzí particle swarm optimalizace na generování víceklasifikátorových systémů. Konkrétně se zabýváme použitím spojitého PSO pro optimalizaci lineární kombinace výstupů klasifkátorů, optimalizací výběru klasifikátorů pro majoritní volbu a optimalizací víceúrovňové sítě s majoritní volbou. In this paper we present application of various versions of the particle swarm optimization method (PSO) in the process of generation of multiple-classifer systems (MCS). While some of the investigated optimisation problems naturally lend themselves to the type of optimisation for which PSO is most suitable we present some other applications requiring non-standard representation of the particles as well as handling of constraints in the optimisation process. In the most typical optimisation case the continuous version of PSO has been successfully applied for the optimization of a soft-linear combiner. On the other hand, one of the adapted binary versions of PSO has been shown to work well in the case of multi-stage organization of majority voting (MOMV), where the search dimension is high and the local search techniques can often get stuck in local optima. In this paper we present application of various versions of the particle swarm optimization method (PSO) in the process of generation of multiple-classifer systems (MCS). While some of the investigated optimisation problems naturally lend themselves to the type of optimisation for which PSO is most suitable we present some other applications requiring non-standard representation of the particles as well as handling of constraints in the optimisation process. In the most typical optimisation case the continuous version of PSO has been successfully applied for the optimization of a soft-linear combiner. On the other hand, one of the adapted binary versions of PSO has been shown to work well in the case of multi-stage organization of majority voting (MOMV), where the search dimension is high and the local search techniques can often get stuck in local optima.
dcterms:title
Particle swarm optimalizace víceklasifikátorových systémů Particle Swarm Optimization of Multiple Classifier Systems Particle Swarm Optimization of Multiple Classifier Systems
skos:prefLabel
Particle swarm optimalizace víceklasifikátorových systémů Particle Swarm Optimization of Multiple Classifier Systems Particle Swarm Optimization of Multiple Classifier Systems
skos:notation
RIV/68407700:21230/07:03132430!RIV08-MSM-21230___
n3:strany
333;340
n3:aktivita
n17:Z
n3:aktivity
Z(MSM6840770012)
n3:dodaniDat
n16:2008
n3:domaciTvurceVysledku
n12:6579191 n12:9431446
n3:druhVysledku
n14:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
440716
n3:idVysledku
RIV/68407700:21230/07:03132430
n3:jazykVysledku
n10:eng
n3:klicovaSlova
binary; metaheuristics; optimization; social impact; social psychology
n3:klicoveSlovo
n7:binary n7:social%20psychology n7:social%20impact n7:metaheuristics n7:optimization
n3:kontrolniKodProRIV
[F90909194C99]
n3:mistoKonaniAkce
San Sebastian
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Computational and Ambient Intelligence
n3:obor
n15:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n16:2007
n3:tvurceVysledku
Lhotská, Lenka Macaš, Martin Gabrys, B. Ruta, D.
n3:typAkce
n18:WRD
n3:zahajeniAkce
2007-06-20+02:00
n3:zamer
n13:MSM6840770012
s:numberOfPages
8
n5:hasPublisher
Springer-Verlag
n9:isbn
978-3-540-73006-4
n11:organizacniJednotka
21230