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Description
  • Funkční ekvivalence dopředných sítí byla navržena za účelem redukce prohledávacích prostorů učících algoritmů. Popis tříd ekvivalence přinesl charakterizaci jednoznačných zástupců parametrizací. Navrhujeme nový genetický učící algoritmus pro RBF a perceptronové sítě s jednou skrytou vrstvou. Experimenty ukazují, že tato procedura urychluje standardní genetické učení. Dle teoretických výsledků je tento přístup použitelný i pro aproximační problémy. (cs)
  • A functional equivalence of feed-forward networks has been proposed to reduce the search space of learning algorithms. The description of equivalence classes has been used to introduce a unique parametrization property and consequently the so-called canonical parametrizations as representatives of functional equivalence classes. A novel genetic learning algorithm for RBF networks and perceptrons with one hidden layer that operates only on these parametrizations has been proposed. Experimental results show that our procedure outperforms the standard genetic learning. An important extension of theoretical results demonstrates that our approach is also valid in the case of approximation.
  • A functional equivalence of feed-forward networks has been proposed to reduce the search space of learning algorithms. The description of equivalence classes has been used to introduce a unique parametrization property and consequently the so-called canonical parametrizations as representatives of functional equivalence classes. A novel genetic learning algorithm for RBF networks and perceptrons with one hidden layer that operates only on these parametrizations has been proposed. Experimental results show that our procedure outperforms the standard genetic learning. An important extension of theoretical results demonstrates that our approach is also valid in the case of approximation. (en)
Title
  • Fee-Forward Neural Networks and Minimal Search Space Learning
  • Dopředné neuronové sítě a učení na minimálních prohledávacích prostorech (cs)
  • Fee-Forward Neural Networks and Minimal Search Space Learning (en)
skos:prefLabel
  • Fee-Forward Neural Networks and Minimal Search Space Learning
  • Dopředné neuronové sítě a učení na minimálních prohledávacích prostorech (cs)
  • Fee-Forward Neural Networks and Minimal Search Space Learning (en)
skos:notation
  • RIV/67985807:_____/05:00405661!RIV06-AV0-67985807
http://linked.open.../vavai/riv/strany
  • 1867;1872
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/05/0557), Z(AV0Z10300504)
http://linked.open...iv/cisloPeriodika
  • 12
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
  • 521485
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/05:00405661
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • search space; feed-forward networks; genetic algorithms (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [512150618B6D]
http://linked.open...i/riv/nazevZdroje
  • WSEAS Transactions on Computers
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
  • 4
http://linked.open...iv/tvurceVysledku
  • Neruda, Roman
http://linked.open...n/vavai/riv/zamer
issn
  • 1109-2750
number of pages
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