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Cost functions based on different types of distance measurements for pseudo neural network synthesis

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dc.title Cost functions based on different types of distance measurements for pseudo neural network synthesis en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.relation.ispartof Mendel 2015: Recent Advances in Soft Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-331919823-1
dc.identifier.isbn 978-3-319-19824-8
dc.date.issued 2015
utb.relation.volume 378
dc.citation.spage 291
dc.citation.epage 301
dc.event.title 21st International Conference on Soft Computing, Mendel 2015
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2015-06-23
dc.event.edate 2015-06-25
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-19824-8_24
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-19824-8_24
dc.subject Chebyshev distance en
dc.subject Classification en
dc.subject Euclidean distance en
dc.subject Manhattan distance en
dc.subject Pseudo neural networks en
dc.subject Symbolic regression en
dc.description.abstract This research deals with a novel approach to classification. New classifiers are synthesized as a complex structure via evolutionary symbolic computation techniques. Compared to previous research, this paper synthesizes multi-input-multi-output (MIMO) classifiers with different cost function based on distance measurements. An inspiration for this work came from the field of artificial neural networks (ANN). 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. Distances used in cost functions were: Manhattan (absolute distances of output vectors), Euclidean, Chebyshev (maximum distance value), Canberra distance, Bray – Curtis. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used Iris data (a known benchmark for classifiers) was used for testing of the proposed method. © Springer International Publishing Switzerland 2015. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1005769
utb.identifier.obdid 43874343
utb.identifier.scopus 2-s2.0-84946741320
utb.identifier.wok 000364847700024
utb.source d-scopus
dc.date.accessioned 2016-01-15T10:59:50Z
dc.date.available 2016-01-15T10:59:50Z
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Zuzana Kominkova Oplatkova, Roman 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 e-mail: senkerik@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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