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Statements

Subject Item
n2:RIV%2F67985807%3A_____%2F13%3A00428789%21RIV15-AV0-67985807
rdf:type
skos:Concept n18:Vysledek
dcterms:description
We present a multi-objectivization approach to the parameter tuning of RBF networks and multilayer perceptrons. The approach works by adding two new objectives - maximization of kappa statistic and minimization of root mean square error - to the originally single-objective problem of minimizing the classification error of the model. We show the performance of the multi-objectivization approach on five data sets and compare it to a surrogate based single-objective algorithm for the same problem. Moreover, we compare the multi-objectivization approach to two surrogate based approaches - a single-objective one and a multi-objective one. We present a multi-objectivization approach to the parameter tuning of RBF networks and multilayer perceptrons. The approach works by adding two new objectives - maximization of kappa statistic and minimization of root mean square error - to the originally single-objective problem of minimizing the classification error of the model. We show the performance of the multi-objectivization approach on five data sets and compare it to a surrogate based single-objective algorithm for the same problem. Moreover, we compare the multi-objectivization approach to two surrogate based approaches - a single-objective one and a multi-objective one.
dcterms:title
Multi-Objectivization and Surrogate Modelling for Neural Network Hyper-Parameters Tuning Multi-Objectivization and Surrogate Modelling for Neural Network Hyper-Parameters Tuning
skos:prefLabel
Multi-Objectivization and Surrogate Modelling for Neural Network Hyper-Parameters Tuning Multi-Objectivization and Surrogate Modelling for Neural Network Hyper-Parameters Tuning
skos:notation
RIV/67985807:_____/13:00428789!RIV15-AV0-67985807
n3:aktivita
n4:I n4:S n4:P
n3:aktivity
I, P(LD13002), S
n3:dodaniDat
n11:2015
n3:domaciTvurceVysledku
n16:8926050
n3:druhVysledku
n17:D
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
90121
n3:idVysledku
RIV/67985807:_____/13:00428789
n3:jazykVysledku
n21:eng
n3:klicovaSlova
multi-objective optimization; parameter tuning; neural networks; surrogate modelling; multi-objectivization
n3:klicoveSlovo
n10:parameter%20tuning n10:surrogate%20modelling n10:neural%20networks n10:multi-objective%20optimization n10:multi-objectivization
n3:kontrolniKodProRIV
[FA09865F211F]
n3:mistoKonaniAkce
Nanning
n3:mistoVydani
Berlin
n3:nazevZdroje
Emerging Intelligent Computing Technology and Applications
n3:obor
n12:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:projekt
n20:LD13002
n3:rokUplatneniVysledku
n11:2013
n3:tvurceVysledku
Neruda, Roman Pilát, M.
n3:typAkce
n7:WRD
n3:zahajeniAkce
2013-07-28+02:00
s:issn
1865-0929
s:numberOfPages
6
n15:doi
10.1007/978-3-642-39678-6_11
n19:hasPublisher
Springer-Verlag
n14:isbn
978-3-642-39677-9