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MIMO pseudo neural networks for iris data classification

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dc.title MIMO pseudo neural networks for iris data classification en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Modern Trends and Techniques in Computer Science (CSOC 2014)
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-331906739-1
dc.date.issued 2014
utb.relation.volume 285
dc.citation.spage 165
dc.citation.epage 176
dc.event.title 3rd Computer Science On-line Conference, CSOC 2014
dc.event.sdate 2014-04-28
dc.event.edate 2014-04-30
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer-Verlag
dc.identifier.doi 10.1007/978-3-319-06740-7_15
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-06740-7_15
dc.subject Classification en
dc.subject Pseudo neural networks en
dc.subject Symbolic regression en
dc.description.abstract This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure which serves as a classifier. Compared to previous research, this paper synthesizes multi-input–multi-output (MIMO) classifiers. Classical artificial neural networks (ANN) were an inspiration for this work. The proposed technique creates a relation between inputs and outputs as a whole structure together with numerical values which could be observed as weights in ANN. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. Iris data (a known benchmark for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for metaevolution version of analytic programming was used. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004200
utb.identifier.obdid 43871859
utb.identifier.scopus 2-s2.0-84923811247
utb.identifier.wok 000370620000015
utb.source d-scopus
dc.date.accessioned 2015-05-06T06:58:24Z
dc.date.available 2015-05-06T06:58:24Z
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Zuzana Kominkova Oplatkova and Roman Senkerik Z. K. Oplatkova (&) R. Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam. T. G. Masaryka 5555, 760 01 Zlin, Czech Republic e-mail: oplatkova@fai.utb.cz R. Senkerik e-mail: senkerik@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089.
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