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Description
  • Classification of text documents is challenging problem not only when browsing the web. Structure representation of documents is necessary to build up appropriate classifier. Unfortunately the document-term matrices are usually so sparse and of high dimensionality due large number of terms representing usually smaller number of the documents. Thus the suitable dimensionality reduction technique is required to be able to develop the classifier. The article deals with supervised extraction method that results to small number of sensitive features derived from the initial document-term matrix. The extraction process simulated by neural network is remarkably fast and utilizes all available supervised information from training data.
  • Classification of text documents is challenging problem not only when browsing the web. Structure representation of documents is necessary to build up appropriate classifier. Unfortunately the document-term matrices are usually so sparse and of high dimensionality due large number of terms representing usually smaller number of the documents. Thus the suitable dimensionality reduction technique is required to be able to develop the classifier. The article deals with supervised extraction method that results to small number of sensitive features derived from the initial document-term matrix. The extraction process simulated by neural network is remarkably fast and utilizes all available supervised information from training data. (en)
Title
  • FAST SUPERVISED FEATURE EXTRACTION FROM STRUCTURED REPRESENTATION OF TEXT DATA
  • FAST SUPERVISED FEATURE EXTRACTION FROM STRUCTURED REPRESENTATION OF TEXT DATA (en)
skos:prefLabel
  • FAST SUPERVISED FEATURE EXTRACTION FROM STRUCTURED REPRESENTATION OF TEXT DATA
  • FAST SUPERVISED FEATURE EXTRACTION FROM STRUCTURED REPRESENTATION OF TEXT DATA (en)
skos:notation
  • RIV/68407700:21240/10:00171783!RIV11-MSM-21240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S, Z(MSM6840770012)
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
  • 258838
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21240/10:00171783
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • document classification; term; neural network (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D54E2906AEC6]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Prague
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, Vol. 2: Full Papers
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Skrbek, Miroslav
  • Háva, Ondřej
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Department of Computer Science and Engineering, FEE, CTU in Prague
https://schema.org/isbn
  • 978-80-01-04589-3
http://localhost/t...ganizacniJednotka
  • 21240
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