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Statements

Subject Item
n2:RIV%2F61389021%3A_____%2F13%3A00421353%21RIV14-AV0-61389021
rdf:type
skos:Concept n9:Vysledek
dcterms:description
Machine learning tools have been used since a long time ago to study disruptions and to predict their occurrence. On the other hand, the challenges posed by the quality and quantities of the data available remain substantial. In this paper, methods to optimize the training data set and the potential of kernels-based advanced machine learning tools are explored and assessed. Various alternatives, ranging from appropriate selection of the weights to the inclusion of artificial points, are investigated to improve the quality of the training data set. Support vector machines (SVM), relevance vector machines (RVMs), and one-class SVM are compared. The relative performances of the different approaches are initially assessed using synthetic data. Then they are applied to a relatively large database of JET disruptions. It is shown that in terms of final results, the optimization of the training databases proved to be very productive. Machine learning tools have been used since a long time ago to study disruptions and to predict their occurrence. On the other hand, the challenges posed by the quality and quantities of the data available remain substantial. In this paper, methods to optimize the training data set and the potential of kernels-based advanced machine learning tools are explored and assessed. Various alternatives, ranging from appropriate selection of the weights to the inclusion of artificial points, are investigated to improve the quality of the training data set. Support vector machines (SVM), relevance vector machines (RVMs), and one-class SVM are compared. The relative performances of the different approaches are initially assessed using synthetic data. Then they are applied to a relatively large database of JET disruptions. It is shown that in terms of final results, the optimization of the training databases proved to be very productive.
dcterms:title
Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies
skos:prefLabel
Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies
skos:notation
RIV/61389021:_____/13:00421353!RIV14-AV0-61389021
n9:predkladatel
n18:ico%3A61389021
n3:aktivita
n16:P n16:I
n3:aktivity
I, P(GAP205/10/2055)
n3:cisloPeriodika
7
n3:dodaniDat
n4:2014
n3:domaciTvurceVysledku
n8:9507507 n8:9079831
n3:druhVysledku
n17:J
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
66135
n3:idVysledku
RIV/61389021:_____/13:00421353
n3:jazykVysledku
n13:eng
n3:klicovaSlova
Learning Machines; Support Vector Machines; Neural Network; ASDEX Upgrade; JET; Disruption mitigation; Tokamaks; ITER
n3:klicoveSlovo
n7:Learning%20Machines n7:JET n7:Tokamaks n7:Disruption%20mitigation n7:ITER n7:ASDEX%20Upgrade n7:Support%20Vector%20Machines n7:Neural%20Network
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[AC43149302C2]
n3:nazevZdroje
IEEE Transactions on Plasma Science
n3:obor
n10:BL
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n11:GAP205%2F10%2F2055
n3:rokUplatneniVysledku
n4:2013
n3:svazekPeriodika
41
n3:tvurceVysledku
Murari, A. Mlynář, Jan Odstrčil, Michal
n3:wos
000321625400009
s:issn
0093-3813
s:numberOfPages
9
n12:doi
10.1109/TPS.2013.2264880