About: Data Adjustment for the Purposes of Self-Teaching of the Neural Network, and its Application for the Model-Reduction of Classification of Patients Suffering from the Ischemic Heart Disease

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About: Data Adjustment for the Purposes of Self-Teaching of the Neural Network, and its Application for the Model-Reduction of Classification of Patients Suffering from the Ischemic Heart Disease 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)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Neural networks present a modern, very effective and practical instrument designated for decision-making support. To make use of them, we not only need to select the neural network type and structure, but also a corresponding data adjustment. One consequence of unsuitable data use can be an inexact or absolutely mistaken function of the model. The need for a certain adjustment of input data comes from the features of the chosen neural network type, from the use of various metrics systems of object attributes, but also from the weight, i.e., the importance of individual attributes, but also from establishing representatives of classifying sets and learning about their characteristics. For the purposes of the classification itself, we can suffice with a model in which the number of output neurons equals the number of classifying sets. Nonetheless, the model with a greater number of neurons assembled into a matrix can testify more about the problem, and provides clearer visual information. Neural networks present a modern, very effective and practical instrument designated for decision-making support. To make use of them, we not only need to select the neural network type and structure, but also a corresponding data adjustment. One consequence of unsuitable data use can be an inexact or absolutely mistaken function of the model. The need for a certain adjustment of input data comes from the features of the chosen neural network type, from the use of various metrics systems of object attributes, but also from the weight, i.e., the importance of individual attributes, but also from establishing representatives of classifying sets and learning about their characteristics. For the purposes of the classification itself, we can suffice with a model in which the number of output neurons equals the number of classifying sets. Nonetheless, the model with a greater number of neurons assembled into a matrix can testify more about the problem, and provides clearer visual information. (en)
Title	Data Adjustment for the Purposes of Self-Teaching of the Neural Network, and its Application for the Model-Reduction of Classification of Patients Suffering from the Ischemic Heart Disease Data Adjustment for the Purposes of Self-Teaching of the Neural Network, and its Application for the Model-Reduction of Classification of Patients Suffering from the Ischemic Heart Disease (en)
skos:prefLabel	Data Adjustment for the Purposes of Self-Teaching of the Neural Network, and its Application for the Model-Reduction of Classification of Patients Suffering from the Ischemic Heart Disease Data Adjustment for the Purposes of Self-Teaching of the Neural Network, and its Application for the Model-Reduction of Classification of Patients Suffering from the Ischemic Heart Disease (en)
skos:notation	RIV/62156489:43110/13:00199660!RIV14-MSM-43110___
http://linked.open...avai/predkladatel	Provozně ekonomická fakulta
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(MSM6215648904)
http://linked.open...iv/cisloPeriodika	2
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Konečný, Vladimír Trenz, Oldřich
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Mendelova univerzita v Brně / Provozně ekonomická fakulta
http://linked.open...dnocenehoVysledku	67939
http://linked.open...ai/riv/idVysledku	RIV/62156489:43110/13:00199660
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	set representative; self-teaching; data standardization; classification; neural network; data reduction (en)
http://linked.open.../riv/klicoveSlovo	self-teaching set representative classification neural network data standardization data reduction
http://linked.open...odStatuVydavatele	CZ - Česká republika
http://linked.open...ontrolniKodProRIV	[BE4A56FD15F0]
http://linked.open...i/riv/nazevZdroje	Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	61
http://linked.open...iv/tvurceVysledku	Konečný, Vladimír Sepši, Milan Trenz, Oldřich
http://linked.open...n/vavai/riv/zamer	The Czech Economy in the Process of Integration and Globalisation, and the Development of Agricultural Sector and the Sector of Services under the New Conditions of the Integrated European Market
issn	1211-8516
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.11118/actaun201361020377
http://localhost/t...ganizacniJednotka	43110

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