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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F05%3APU50474%21RIV06-GA0-26220___
rdf:type
n8:Vysledek skos:Concept
dcterms:description
This article deals with hybrid expert system that has knowledge base realized through a hierarchical structure of artificial neural networks (NN). The decision tree is built by C4.5 algorithm at first. In the next step the nods of the tree are replaced by NN. They are trained to split the data in the same way as the nods. So the problem is separated into partial sub-problems that are solved by individual NN. At the end an expert is requested to change the structure of particular NN according to his knowwledge and experiences. Each NN solves a partial sub-problem what decreases demands upon the capability of an expert and accelerates the time needed to harmonize the knowledge base. Unlike the traditional expert system, the output of this architecture isnot classification, however we receive a list of hypotheses evaluated by certain value of the hypothesis trust. This article deals with hybrid expert system that has knowledge base realized through a hierarchical structure of artificial neural networks (NN). The decision tree is built by C4.5 algorithm at first. In the next step the nods of the tree are replaced by NN. They are trained to split the data in the same way as the nods. So the problem is separated into partial sub-problems that are solved by individual NN. At the end an expert is requested to change the structure of particular NN according to his knowwledge and experiences. Each NN solves a partial sub-problem what decreases demands upon the capability of an expert and accelerates the time needed to harmonize the knowledge base. Unlike the traditional expert system, the output of this architecture isnot classification, however we receive a list of hypotheses evaluated by certain value of the hypothesis trust. This article deals with hybrid expert system that has knowledge base realized through a hierarchical structure of artificial neural networks (NN). The decision tree is built by C4.5 algorithm at first. In the next step the nods of the tree are replaced by NN. They are trained to split the data in the same way as the nods. So the problem is separated into partial sub-problems that are solved by individual NN. At the end an expert is requested to change the structure of particular NN according to his knowwledge and experiences. Each NN solves a partial sub-problem what decreases demands upon the capability of an expert and accelerates the time needed to harmonize the knowledge base. Unlike the traditional expert system, the output of this architecture isnot classification, however we receive a list of hypotheses evaluated by certain value of the hypothesis trust.
dcterms:title
Hybrid Expert System Hybrid Expert System Hybrid Expert System
skos:prefLabel
Hybrid Expert System Hybrid Expert System Hybrid Expert System
skos:notation
RIV/00216305:26220/05:PU50474!RIV06-GA0-26220___
n3:strany
95-97
n3:aktivita
n15:P
n3:aktivity
P(GA102/03/1097), P(GA102/05/0663)
n3:cisloPeriodika
7
n3:dodaniDat
n11:2006
n3:domaciTvurceVysledku
n6:7776756 n6:8359474
n3:druhVysledku
n12:J
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
523909
n3:idVysledku
RIV/00216305:26220/05:PU50474
n3:jazykVysledku
n18:eng
n3:klicovaSlova
expert system, connectionist expert system, knowledge base, neural network, decision tree
n3:klicoveSlovo
n4:decision%20tree n4:neural%20network n4:expert%20system n4:connectionist%20expert%20system n4:knowledge%20base
n3:kodStatuVydavatele
GR - Řecká republika
n3:kontrolniKodProRIV
[687282BF01BB]
n3:nazevZdroje
WSEAS Transactions on Information Science and Applications
n3:obor
n14:BD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n7:GA102%2F03%2F1097 n7:GA102%2F05%2F0663
n3:rokUplatneniVysledku
n11:2005
n3:svazekPeriodika
NEUVEDEN
n3:tvurceVysledku
Honzík, Petr Jirsík, Václav
s:issn
1790-0832
s:numberOfPages
3
n16:organizacniJednotka
26220