About: Spatio-Temporal Data Classification using CVNNs

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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
rdfs:seeAlso	http://www.sciencedirect.com/science/article/pii/S1569190X12001347
Description	This paper presents two new approaches of spatio-temporal data classification using complex-valued neural networks. First approach uses extended complex-valued backpropagation algorithm to train MLP network, whose output’s amplitudes are encoded in one-of-N coding. It makes a classification decision based on accumulated distance between network output and trained pattern. The second approach is inspired in RBF networks with two layer architecture. Neurons from the first layer have fixed position in space and time encoded into theirs weights. This layer is trained by presented extension of neural gas algorithm into complex numbers. The second layer affects which neurons from the first layer belong to specific class. Paper contains details on experimenting with proposed approaches on artificial data of hand-written character recognition and comparison of both methods. This paper presents two new approaches of spatio-temporal data classification using complex-valued neural networks. First approach uses extended complex-valued backpropagation algorithm to train MLP network, whose output’s amplitudes are encoded in one-of-N coding. It makes a classification decision based on accumulated distance between network output and trained pattern. The second approach is inspired in RBF networks with two layer architecture. Neurons from the first layer have fixed position in space and time encoded into theirs weights. This layer is trained by presented extension of neural gas algorithm into complex numbers. The second layer affects which neurons from the first layer belong to specific class. Paper contains details on experimenting with proposed approaches on artificial data of hand-written character recognition and comparison of both methods. (en)
Title	Spatio-Temporal Data Classification using CVNNs Spatio-Temporal Data Classification using CVNNs (en)
skos:prefLabel	Spatio-Temporal Data Classification using CVNNs Spatio-Temporal Data Classification using CVNNs (en)
skos:notation	RIV/68407700:21240/13:00203405!RIV14-MSM-21240___
http://linked.open...avai/riv/aktivita	I S Z
http://linked.open...avai/riv/aktivity	I, S, Z(MSM6840770012)
http://linked.open...iv/cisloPeriodika	33
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Zahradník, Jakub Skrbek, Miroslav
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	České vysoké učení technické v Praze / Fakulta informačních technologií
http://linked.open...dnocenehoVysledku	106815
http://linked.open...ai/riv/idVysledku	RIV/68407700:21240/13:00203405
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Complex-valued; Artificial neural network; Spatio-temporal; Neural gas; Classification; Back-propagation (en)
http://linked.open.../riv/klicoveSlovo	Artificial neural network Back-propagation Complex-valued Neural gas Spatio-temporal Classification
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[D121EA0E4B6E]
http://linked.open...i/riv/nazevZdroje	Simulation Modelling Practice and Theory
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	33
http://linked.open...iv/tvurceVysledku	Skrbek, Miroslav Zahradník, Jakub
http://linked.open...ain/vavai/riv/wos	000317253700007
http://linked.open...n/vavai/riv/zamer	Transdisciplinární výzkum v oblasti biomedicínského inženýrství II
issn	1569-190X
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.simpat.2012.10.001
http://localhost/t...ganizacniJednotka	21240

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