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  • 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. (en)
Title
  • Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion
  • Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion (en)
skos:prefLabel
  • Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion
  • Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion (en)
skos:notation
  • RIV/00216305:26230/11:PU95990!RIV13-MSM-26230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S, Z(MSM0021630528)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 201703
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26230/11:PU95990
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Digit Recognition, Classifier Fusion, Multiresolution (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [74572F2704F4]
http://linked.open...v/mistoKonaniAkce
  • Las Palmas de Gran Canaria
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • Proceedings of IbPRIA 2011, LNCS
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Havel, Jiří
  • Herout, Adam
  • Štrba, Miroslav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-21256-7
http://localhost/t...ganizacniJednotka
  • 26230
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