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Description
  • We introduce a notion of so called univalent neural networks realizing injective mappingand sharing the input and output space. First, we postulate necessary and sufficient conditions of univalence and derive several models of univalent nets. Then explore learning algorithms that could be used for the defined network class - special variants of backpropagation learning.
  • We introduce a notion of so called univalent neural networks realizing injective mappingand sharing the input and output space. First, we postulate necessary and sufficient conditions of univalence and derive several models of univalent nets. Then explore learning algorithms that could be used for the defined network class - special variants of backpropagation learning. (en)
  • V práci je zavedena tzv. univalentní neuronová síť, která realizuje vzájemné jednoznačně zobrazení mezi vstupním a výstupním prostorem. Je postulována nutná a postačující podmínka univalentnosti a navrženo několik typů takovýchto sítí. Pote jsou diskutovány modifikace učících algoritmů, které mohou být použity pro učení těchto sítí, mimo jiné speciální varianta algoritmu back-propagation. (cs)
Title
  • Univalent Neural Nets and Shape Detection
  • Univalentní neuronové sítě pro detekci geometrických tvarů (cs)
  • Univalent Neural Nets and Shape Detection (en)
skos:prefLabel
  • Univalent Neural Nets and Shape Detection
  • Univalentní neuronové sítě pro detekci geometrických tvarů (cs)
  • Univalent Neural Nets and Shape Detection (en)
skos:notation
  • RIV/67985807:_____/08:00312973!RIV09-AV0-67985807
http://linked.open...avai/riv/aktivita
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  • P(1M0567), Z(AV0Z10300504)
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  • 401456
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  • RIV/67985807:_____/08:00312973
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  • data mining; shape detection; modified back-propagation; pattern recognition (en)
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  • [26B2D6EF1BA8]
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  • Shanghai
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  • Piscataway
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  • Signal Image Technology and Internet Based Systems
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  • Hakl, František
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  • 000259670300114
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number of pages
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  • IEEE Computer Society
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  • 978-0-7695-3122-9
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