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Description
  • An assembly neural network based on binary Hebbian rule is suggested for pattern recognition. The network consists of several sub-networks according to the number of classes to be recognized. Each sub-network consists of several neural columns according to dimensionality of signal space so that the value of each signal component is encoded by activity of adjacent neurons of the column. A new recognition algorithm is presented which realizes the nearest-neighbor method in the assembly neural network. Computer simulation of the network is performed. The model is tested on a texture segmentation task. The experiments have demonstrated that the network is able to segment reasonably real-world texture images.
  • An assembly neural network based on binary Hebbian rule is suggested for pattern recognition. The network consists of several sub-networks according to the number of classes to be recognized. Each sub-network consists of several neural columns according to dimensionality of signal space so that the value of each signal component is encoded by activity of adjacent neurons of the column. A new recognition algorithm is presented which realizes the nearest-neighbor method in the assembly neural network. Computer simulation of the network is performed. The model is tested on a texture segmentation task. The experiments have demonstrated that the network is able to segment reasonably real-world texture images. (en)
  • Pro rozpoznávaní vzoru jsou navrhovány skládané neuronové sítě založené na Hebbově učícím pravidle. Síť se skládá z několika subsítí, v závislosti na počtu tříd, které mají být rozpoznány. Každá subsíť se skládá z velkého počtu sloupců, takže hodnota každé komponenty signálu je zakódována pomocí aktivity neuronů v příslušném sloupci. Je presentován nový rozpoznávací algoritmus, který realizuje metodu nejbližšího souseda v prostředí skládaných neuronových sítí. Architektura NN byla testována pomocí simulačního programu na problému segmentace textury. Experimenty prokázaly schopnost sítě rozumně segmentovat reálně existující textury. (cs)
Title
  • Assembly Neural Network with Nearest-Neighbor Recognition Algorithm
  • Skládané neuronové sítě s rozpoznávacím algoritmem nejbližšího souseda (cs)
  • Assembly Neural Network with Nearest-Neighbor Recognition Algorithm (en)
skos:prefLabel
  • Assembly Neural Network with Nearest-Neighbor Recognition Algorithm
  • Skládané neuronové sítě s rozpoznávacím algoritmem nejbližšího souseda (cs)
  • Assembly Neural Network with Nearest-Neighbor Recognition Algorithm (en)
skos:notation
  • RIV/67985807:_____/05:00405597!RIV06-MSM-67985807
http://linked.open.../vavai/riv/strany
  • 9;22
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0567)
http://linked.open...iv/cisloPeriodika
  • -
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
  • 513138
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/05:00405597
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • assembly neural network; unsupervised learning; binary Hebbian rule; pattern recognition; texture segmentation; classification (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [A9DEDB33602F]
http://linked.open...i/riv/nazevZdroje
  • Neural Network World
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 15
http://linked.open...iv/tvurceVysledku
  • Frolov, A.
  • Húsek, Dušan
  • Goltsev, A.
issn
  • 1210-0552
number of pages
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