About: An Extension of the Method for Fuzzy Rules Extraction by Means of Artificial Neural Networks

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: An Extension of the Method for Fuzzy Rules Extraction by Means of Artificial Neural Networks Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Knowledge extraction from data in the form of rules is a widespread di- rection in data mining area, which allows to obtain interesting relationships in data from large databases in for a human easily understandable form. This paper deals withoneofthemethodsforextractionofrulesfromdatawhichextractrulesinform of a formula in considered fuzzy logic by means of artificial neural networks with special architecture. Using artificial neural networks in extraction process, above mentioned methods gain good approximation of analyzed data and thanks to spe- cial architecture allows to extract human-understandable knowledge. The method described in this paper was, however, missing any module, that is a standard part of themostofmethodsusedforrulesextractionfromdata,thatwouldallowtotheuser subjective selection of the best ratio between accuracy and comprehensibility of the model. This is especially important feature for solving data mining tasks called searching of concepts descriptio Knowledge extraction from data in the form of rules is a widespread di- rection in data mining area, which allows to obtain interesting relationships in data from large databases in for a human easily understandable form. This paper deals withoneofthemethodsforextractionofrulesfromdatawhichextractrulesinform of a formula in considered fuzzy logic by means of artificial neural networks with special architecture. Using artificial neural networks in extraction process, above mentioned methods gain good approximation of analyzed data and thanks to spe- cial architecture allows to extract human-understandable knowledge. The method described in this paper was, however, missing any module, that is a standard part of themostofmethodsusedforrulesextractionfromdata,thatwouldallowtotheuser subjective selection of the best ratio between accuracy and comprehensibility of the model. This is especially important feature for solving data mining tasks called searching of concepts descriptio (en)
Title	An Extension of the Method for Fuzzy Rules Extraction by Means of Artificial Neural Networks An Extension of the Method for Fuzzy Rules Extraction by Means of Artificial Neural Networks (en)
skos:prefLabel	An Extension of the Method for Fuzzy Rules Extraction by Means of Artificial Neural Networks An Extension of the Method for Fuzzy Rules Extraction by Means of Artificial Neural Networks (en)
skos:notation	RIV/47813059:19520/13:#0002214!RIV14-MSM-19520___
http://linked.open...avai/riv/aktivita	O
http://linked.open...avai/riv/aktivity	O
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Górecki, Jan Šperka, Roman Spišák, Marek
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Slezská univerzita v Opavě / Obchodně podnikatelská fakulta v Karviné
http://linked.open...dnocenehoVysledku	60360
http://linked.open...ai/riv/idVysledku	RIV/47813059:19520/13:#0002214
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	knowledge extraction from data; comprehensibility and accuracy of data models; artificial neural network; fuzzy logic; disjunctive normal form (en)
http://linked.open.../riv/klicoveSlovo	comprehensibility and accuracy of data models disjunctive normal form knowledge extraction from data artificial neural network fuzzy logic
http://linked.open...ontrolniKodProRIV	[674E654D7BD0]
http://linked.open...v/mistoKonaniAkce	Amsterdam
http://linked.open...i/riv/mistoVydani	Amsterdam
http://linked.open...i/riv/nazevZdroje	Frontiers in Artificial Intelligence and Applications 255
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	GÓRECKI, Jan SPIŠÁK, Marek ŠPERKA, Roman
http://linked.open...vavai/riv/typAkce	EUR - Evropská
http://linked.open.../riv/zahajeniAkce	2013-06-26 (xsd:date)
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.3233/978-1-61499-264-6-65
http://purl.org/ne...btex#hasPublisher	IOS Press BV
https://schema.org/isbn	978-1-61499-263-9
http://localhost/t...ganizacniJednotka	19520

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 48 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software