This HTML5 document contains 50 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n8http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n22http://purl.org/net/nknouf/ns/bibtex#
n14http://localhost/temp/predkladatel/
n15http://linked.opendata.cz/resource/domain/vavai/projekt/
n10http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n19http://linked.opendata.cz/ontology/domain/vavai/
n17https://schema.org/
n4http://linked.opendata.cz/resource/domain/vavai/zamer/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n20http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F08%3A03145893%21RIV09-MSM-21230___/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n5http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n21http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n12http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A03145893%21RIV09-MSM-21230___
rdf:type
skos:Concept n19:Vysledek
dcterms:description
Nowadays a Feature Ranking (FR) is commonly used method for obtaining information about a large data sets with various dimensionality. This knowledge can be used in a next step of data processing. Accuracy and a speed of experiments can be improved by this. Our approach is based on Artificial Neural Networks (ANN) instead of classical statistical methods. We obtain the knowledge as a by-product of Niching Genetic Algorithm (NGA) used for creation of a feedforward hybrid neural network called GAME. In this paper we present a behaviour of FeRaNGA (Feature Ranking method using Niching Genetic Algorithm(NGA)) during a learning process, especially in every layer of generated GAME network. We want to answer how important is NGA configuration and processing procedure for FR results because behaviour of GA is nondeterministic and thereby were results of FeRaNGA also indefinitive. This method ranks features depending on a percentage of processing elements that survived a selection process. Nowadays a Feature Ranking (FR) is commonly used method for obtaining information about a large data sets with various dimensionality. This knowledge can be used in a next step of data processing. Accuracy and a speed of experiments can be improved by this. Our approach is based on Artificial Neural Networks (ANN) instead of classical statistical methods. We obtain the knowledge as a by-product of Niching Genetic Algorithm (NGA) used for creation of a feedforward hybrid neural network called GAME. In this paper we present a behaviour of FeRaNGA (Feature Ranking method using Niching Genetic Algorithm(NGA)) during a learning process, especially in every layer of generated GAME network. We want to answer how important is NGA configuration and processing procedure for FR results because behaviour of GA is nondeterministic and thereby were results of FeRaNGA also indefinitive. This method ranks features depending on a percentage of processing elements that survived a selection process. Nowadays a Feature Ranking (FR) is commonly used method for obtaining information about a large data sets with various dimensionality. This knowledge can be used in a next step of data processing. Accuracy and a speed of experiments can be improved by this. Our approach is based on Artificial Neural Networks (ANN) instead of classical statistical methods. We obtain the knowledge as a by-product of Niching Genetic Algorithm (NGA) used for creation of a feedforward hybrid neural network called GAME. In this paper we present a behaviour of FeRaNGA (Feature Ranking method using Niching Genetic Algorithm(NGA)) during a learning process, especially in every layer of generated GAME network. We want to answer how important is NGA configuration and processing procedure for FR results because behaviour of GA is nondeterministic and thereby were results of FeRaNGA also indefinitive. This method ranks features depending on a percentage of processing elements that survived a selection process.
dcterms:title
Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling
skos:prefLabel
Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling Behaviour of FeRaNGA method for Feature Ranking during learning process using Inductive Modelling
skos:notation
RIV/68407700:21230/08:03145893!RIV09-MSM-21230___
n3:aktivita
n21:Z n21:P
n3:aktivity
P(KJB201210701), Z(MSM6840770012)
n3:dodaniDat
n12:2009
n3:domaciTvurceVysledku
n10:1266500 n10:2874695 n10:7035586
n3:druhVysledku
n9:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
357752
n3:idVysledku
RIV/68407700:21230/08:03145893
n3:jazykVysledku
n7:eng
n3:klicovaSlova
Inductive modelling, Feature Ranking, Artificial Neural Networks, FeRaNGA, Nitching Genetic Algorith
n3:klicoveSlovo
n5:Feature%20Ranking n5:Nitching%20Genetic%20Algorith n5:Inductive%20modelling n5:Artificial%20Neural%20Networks n5:FeRaNGA
n3:kontrolniKodProRIV
[B6D647CDE1D3]
n3:mistoKonaniAkce
Kyjev
n3:mistoVydani
Kiev
n3:nazevZdroje
Proceedings of the 2nd International Conference on Inductive Modelling
n3:obor
n18:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n15:KJB201210701
n3:rokUplatneniVysledku
n12:2008
n3:tvurceVysledku
Kordík, Pavel Šnorek, Miroslav Pilný, Aleš
n3:typAkce
n8:WRD
n3:zahajeniAkce
2008-09-15+02:00
n3:zamer
n4:MSM6840770012
s:numberOfPages
5
n22:hasPublisher
Ukr. INTEI
n17:isbn
978-966-02-4889-2
n14:organizacniJednotka
21230