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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F13%3A00396745%21RIV14-GA0-67985556
rdf:type
n3:Vysledek skos:Concept
dcterms:description
Machine learning (ML) and knowledge discovery both use and serve to decision making (DM), which has to cope with uncertainty, incomplete knowledge, problem and data complexity and imperfection (limited cognitive and evaluating capabilities) of the involved heterogeneous multiple participants (aka agents, decision makers, components, controllers, classifiers, etc.). Contemporary DM deals with complex systems characterised by heterogeneous components and their goal-motivated dynamic interactions. The individual participants are selfish, i.e. follow their individual goals. There is no well-justified way to influence or describe the resulting collective behaviour of such a system via a well-proved combination of the selfish components. Economic and natural sciences describe concepts governing the functioning of systems of selfish participants as well as ways influencing their behaviour. However, the majority of solutions rely on the human moderator/manager controlling such a system. Machine learning (ML) and knowledge discovery both use and serve to decision making (DM), which has to cope with uncertainty, incomplete knowledge, problem and data complexity and imperfection (limited cognitive and evaluating capabilities) of the involved heterogeneous multiple participants (aka agents, decision makers, components, controllers, classifiers, etc.). Contemporary DM deals with complex systems characterised by heterogeneous components and their goal-motivated dynamic interactions. The individual participants are selfish, i.e. follow their individual goals. There is no well-justified way to influence or describe the resulting collective behaviour of such a system via a well-proved combination of the selfish components. Economic and natural sciences describe concepts governing the functioning of systems of selfish participants as well as ways influencing their behaviour. However, the majority of solutions rely on the human moderator/manager controlling such a system.
dcterms:title
Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013) Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013)
skos:prefLabel
Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013) Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013)
skos:notation
RIV/67985556:_____/13:00396745!RIV14-GA0-67985556
n3:predkladatel
n9:ico%3A67985556
n4:aktivita
n7:P n7:I
n4:aktivity
I, P(GA13-13502S)
n4:dodaniDat
n5:2014
n4:domaciTvurceVysledku
n12:4780280 n12:6585256
n4:druhVysledku
n11:O
n4:duvernostUdaju
n17:S
n4:entitaPredkladatele
n16:predkladatel
n4:idSjednocenehoVysledku
104033
n4:idVysledku
RIV/67985556:_____/13:00396745
n4:jazykVysledku
n13:eng
n4:klicovaSlova
scalable; decision making; uncertainty; imperfection; deliberation
n4:klicoveSlovo
n6:decision%20making n6:imperfection n6:uncertainty n6:deliberation n6:scalable
n4:kontrolniKodProRIV
[7619BC525AE5]
n4:obor
n10:BB
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
29
n4:projekt
n15:GA13-13502S
n4:rokUplatneniVysledku
n5:2013
n4:tvurceVysledku
Guy, Tatiana Valentine Kárný, Miroslav