This HTML5 document contains 51 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n9http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n21http://localhost/temp/predkladatel/
n4http://purl.org/net/nknouf/ns/bibtex#
n14http://linked.opendata.cz/resource/domain/vavai/projekt/
n7http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n22http://linked.opendata.cz/resource/domain/vavai/subjekt/
n15http://linked.opendata.cz/ontology/domain/vavai/
n19https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
n5http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F62156489%3A43110%2F12%3A00190768%21RIV13-GA0-43110___/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n8http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n20http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n16http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n6http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F62156489%3A43110%2F12%3A00190768%21RIV13-GA0-43110___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
In this article, we aim to compare different methods usable for solving classification problems. A substantial number of methods that are not based on mathematical statistics may be used. Exploring these methods is interesting, because they are often capable of solving problems, which are not easily solvable using classificators based purely on mathematical statistics. There are many approaches available such as support vector machines, neural networks, evolutionary algorithms, parallel coordinates, etc. In this article, we concentrate on describing different neural network approaches, parallel coordinates and genetic algorithms. Neural networks come in many flavors (e.g. multi-layer perceptron, non-linear autoregressive networks) and they have achieved some recognition. Genetic algorithms also have been used for classification many times before, but with mixed results. In this article, we describe and evaluate different capabilities of these methods when used for economic data. This for example includes identification of hidden data structures, dealing with outliers and noise. In this article, we aim to compare different methods usable for solving classification problems. A substantial number of methods that are not based on mathematical statistics may be used. Exploring these methods is interesting, because they are often capable of solving problems, which are not easily solvable using classificators based purely on mathematical statistics. There are many approaches available such as support vector machines, neural networks, evolutionary algorithms, parallel coordinates, etc. In this article, we concentrate on describing different neural network approaches, parallel coordinates and genetic algorithms. Neural networks come in many flavors (e.g. multi-layer perceptron, non-linear autoregressive networks) and they have achieved some recognition. Genetic algorithms also have been used for classification many times before, but with mixed results. In this article, we describe and evaluate different capabilities of these methods when used for economic data. This for example includes identification of hidden data structures, dealing with outliers and noise.
dcterms:title
Comparison of Different Non-statistical Classification Methods Comparison of Different Non-statistical Classification Methods
skos:prefLabel
Comparison of Different Non-statistical Classification Methods Comparison of Different Non-statistical Classification Methods
skos:notation
RIV/62156489:43110/12:00190768!RIV13-GA0-43110___
n15:predkladatel
n22:orjk%3A43110
n3:aktivita
n16:P
n3:aktivity
P(GAP403/11/2085)
n3:dodaniDat
n6:2013
n3:domaciTvurceVysledku
n7:8810419 n7:5759269 n7:9234608 n7:5623979 n7:3837726
n3:druhVysledku
n11:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n5:predkladatel
n3:idSjednocenehoVysledku
127903
n3:idVysledku
RIV/62156489:43110/12:00190768
n3:jazykVysledku
n13:eng
n3:klicovaSlova
corporate performance; parallel coordinates; decision trees; sustainability reporting; neural networks; classification
n3:klicoveSlovo
n8:decision%20trees n8:neural%20networks n8:corporate%20performance n8:classification n8:parallel%20coordinates n8:sustainability%20reporting
n3:kontrolniKodProRIV
[B1AC0966BC1C]
n3:mistoKonaniAkce
Karviná
n3:mistoVydani
Karviná
n3:nazevZdroje
Proceedings of the 30th International Conference Mathematical Methods in Economics 2012
n3:obor
n18:IN
n3:pocetDomacichTvurcuVysledku
5
n3:pocetTvurcuVysledku
5
n3:projekt
n14:GAP403%2F11%2F2085
n3:rokUplatneniVysledku
n6:2012
n3:tvurceVysledku
Popelka, Ondřej Hodinka, Michal Hřebíček, Jiří Trenz, Oldřich Štencl, Michael
n3:typAkce
n9:WRD
n3:zahajeniAkce
2012-01-01+01:00
s:numberOfPages
6
n4:hasPublisher
Slezská univerzita v Opavě. Obchodně podnikatelská fakulta v Karviné
n19:isbn
978-80-7248-779-0
n21:organizacniJednotka
43110