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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F11%3APU95990%21RIV13-MSM-26230___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
One common approach to construction of highly accurate classifiers for hadwritten digit recognition is fusion of several weaker classifiers into a compound one, which (when meeting some constraints) outperforms all the individual fused classifiers.  This paper studies the possibility of fusing classifiers of different kinds (Self-Organizing Maps, Randomized Trees, and AdaBoost with MB-LBP weak hypotheses) constructed on training sets resampled to different resolutions.  While it is common to select one resolution of the input samples as the ``ideal one'' and fuse classifiers constructed for it, this paper shows that the accuracy of classification can be improved by fusing information from several scales. One common approach to construction of highly accurate classifiers for hadwritten digit recognition is fusion of several weaker classifiers into a compound one, which (when meeting some constraints) outperforms all the individual fused classifiers.  This paper studies the possibility of fusing classifiers of different kinds (Self-Organizing Maps, Randomized Trees, and AdaBoost with MB-LBP weak hypotheses) constructed on training sets resampled to different resolutions.  While it is common to select one resolution of the input samples as the ``ideal one'' and fuse classifiers constructed for it, this paper shows that the accuracy of classification can be improved by fusing information from several scales.
dcterms:title
Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion
skos:prefLabel
Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion
skos:notation
RIV/00216305:26230/11:PU95990!RIV13-MSM-26230___
n7:predkladatel
n19:orjk%3A26230
n4:aktivita
n9:Z n9:S
n4:aktivity
S, Z(MSM0021630528)
n4:dodaniDat
n12:2013
n4:domaciTvurceVysledku
n10:1211811 n10:1958313 Štrba, Miroslav
n4:druhVysledku
n18:D
n4:duvernostUdaju
n5:S
n4:entitaPredkladatele
n16:predkladatel
n4:idSjednocenehoVysledku
201703
n4:idVysledku
RIV/00216305:26230/11:PU95990
n4:jazykVysledku
n17:eng
n4:klicovaSlova
Digit Recognition, Classifier Fusion, Multiresolution
n4:klicoveSlovo
n13:Multiresolution n13:Classifier%20Fusion n13:Digit%20Recognition
n4:kontrolniKodProRIV
[74572F2704F4]
n4:mistoKonaniAkce
Las Palmas de Gran Canaria
n4:mistoVydani
Berlin
n4:nazevZdroje
Proceedings of IbPRIA 2011, LNCS
n4:obor
n14:IN
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n12:2011
n4:tvurceVysledku
Štrba, Miroslav Herout, Adam Havel, Jiří
n4:typAkce
n22:WRD
n4:zahajeniAkce
2011-06-08+02:00
n4:zamer
n20:MSM0021630528
s:numberOfPages
8
n11:hasPublisher
Springer-Verlag
n3:isbn
978-3-642-21256-7
n15:organizacniJednotka
26230