About: Pseudo Neural Networks for Iris Data Classification

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	This research deals with a novel approach to classification. Iris data was used for the experiments. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, was an inspiration for this work. Artificial neural networks need to optimize weights, but the structure and transfer functions are usually set up before the training. The proposed method utilizes the symbolic regression for synthesis of a whole structure, i.e. the relation between inputs and output(s). This paper differs from the previous approach where only one output pseudo node was used even for more classes. In this case, there were synthesized more node output equations as in classical artificial neural networks. The benchmark was iris data as in previous research. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used. This research deals with a novel approach to classification. Iris data was used for the experiments. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, was an inspiration for this work. Artificial neural networks need to optimize weights, but the structure and transfer functions are usually set up before the training. The proposed method utilizes the symbolic regression for synthesis of a whole structure, i.e. the relation between inputs and output(s). This paper differs from the previous approach where only one output pseudo node was used even for more classes. In this case, there were synthesized more node output equations as in classical artificial neural networks. The benchmark was iris data as in previous research. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used. (en)
Title	Pseudo Neural Networks for Iris Data Classification Pseudo Neural Networks for Iris Data Classification (en)
skos:prefLabel	Pseudo Neural Networks for Iris Data Classification Pseudo Neural Networks for Iris Data Classification (en)
skos:notation	RIV/70883521:28140/14:43871643!RIV15-MSM-28140___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED2.1.00/03.0089)
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Oplatková, Zuzana Šenkeřík, Roman Komínek, Aleš
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	Univerzita Tomáše Bati ve Zlíně / Fakulta aplikované informatiky
http://linked.open...dnocenehoVysledku	40772
http://linked.open...ai/riv/idVysledku	RIV/70883521:28140/14:43871643
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	classification.; symbolic regression; Pseudo neural networks (en)
http://linked.open.../riv/klicoveSlovo	Pseudo neural networks classification. symbolic regression
http://linked.open...ontrolniKodProRIV	[525B9BA23396]
http://linked.open...v/mistoKonaniAkce	Brescia
http://linked.open...i/riv/mistoVydani	Nottingham
http://linked.open...i/riv/nazevZdroje	28th European Conference on Modelling and Simulation
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...vavai/riv/projekt	The Centre of Security, Information and Advanced Technologies (CEBIA-Tech)
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Šenkeřík, Roman Komínková Oplatková, Zuzana Komínek, Aleš
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-05-27 (xsd:date)
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.7148/2014-0387
http://purl.org/ne...btex#hasPublisher	ECMS
https://schema.org/isbn	978-0-9564944-8-1
http://localhost/t...ganizacniJednotka	28140

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