This HTML5 document contains 45 embedded RDF statements represented using HTML+Microdata notation.

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

Namespace Prefixes

PrefixIRI
n6http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n12http://localhost/temp/predkladatel/
n11http://purl.org/net/nknouf/ns/bibtex#
n18http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n9http://linked.opendata.cz/resource/domain/vavai/projekt/
n17http://linked.opendata.cz/ontology/domain/vavai/
n16https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n21http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216275%3A25410%2F10%3A39881985%21RIV11-GA0-25410___/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n20http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n13http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n19http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n15http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n14http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216275%3A25410%2F10%3A39881985%21RIV11-GA0-25410___
rdf:type
skos:Concept n17:Vysledek
dcterms:description
Fiscal stress has forced municipalities to pay increasing attention to the importance of revenue prediction. Currently, econometric models and expert opinions are used for municipal revenue prediction. In this paper we present a design of support vector machine ensembles for the prediction of municipal revenue. Linear regression model and feed-forward neural network ensembles are used as benchmark methods. We prove that stochastic gradient boosting outperforms the other methods when creating SVM ensembles for this regression problem. Further, bagging shows best performance for feed-forward neural network ensembles, and dagging is preferable for linear regression model ensembles. Fiscal stress has forced municipalities to pay increasing attention to the importance of revenue prediction. Currently, econometric models and expert opinions are used for municipal revenue prediction. In this paper we present a design of support vector machine ensembles for the prediction of municipal revenue. Linear regression model and feed-forward neural network ensembles are used as benchmark methods. We prove that stochastic gradient boosting outperforms the other methods when creating SVM ensembles for this regression problem. Further, bagging shows best performance for feed-forward neural network ensembles, and dagging is preferable for linear regression model ensembles.
dcterms:title
Municipal Revenue Prediction by Support Vector Machine Ensembles Municipal Revenue Prediction by Support Vector Machine Ensembles
skos:prefLabel
Municipal Revenue Prediction by Support Vector Machine Ensembles Municipal Revenue Prediction by Support Vector Machine Ensembles
skos:notation
RIV/00216275:25410/10:39881985!RIV11-GA0-25410___
n3:aktivita
n7:P
n3:aktivity
P(GA402/08/0849), P(GP402/09/P090)
n3:dodaniDat
n14:2011
n3:domaciTvurceVysledku
n18:7141319 n18:9558020
n3:druhVysledku
n15:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
273232
n3:idVysledku
RIV/00216275:25410/10:39881985
n3:jazykVysledku
n13:eng
n3:klicovaSlova
neural network ensembles; modelling; support vector machine ensembles; regression; prediction; Municipal revenue
n3:klicoveSlovo
n4:support%20vector%20machine%20ensembles n4:Municipal%20revenue n4:prediction n4:neural%20network%20ensembles n4:regression n4:modelling
n3:kontrolniKodProRIV
[AF312899154E]
n3:mistoKonaniAkce
Corfu
n3:mistoVydani
Atény
n3:nazevZdroje
Latest Trends on Computers
n3:obor
n19:AE
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n9:GA402%2F08%2F0849 n9:GP402%2F09%2FP090
n3:rokUplatneniVysledku
n14:2010
n3:tvurceVysledku
Hájek, Petr Olej, Vladimír
n3:typAkce
n6:EUR
n3:zahajeniAkce
2010-07-23+02:00
s:numberOfPages
6
n11:hasPublisher
WSEAS Press
n16:isbn
978-960-474-201-1
n12:organizacniJednotka
25410