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Description
  • Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, although the presence of metaparameters is their drawback. The metaparameters are typically supposed to be given in advance and come ready as an input of the algorithm. Typically, they are set based on the task context by an experienced user. In this paper, we develop a method for finding optimal values of metaparameters, namely the type of kernel function, kernel parameters and regularization parameter. The method is based on co-evolutionary genetic algorithms with different species for different kind
  • Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, although the presence of metaparameters is their drawback. The metaparameters are typically supposed to be given in advance and come ready as an input of the algorithm. Typically, they are set based on the task context by an experienced user. In this paper, we develop a method for finding optimal values of metaparameters, namely the type of kernel function, kernel parameters and regularization parameter. The method is based on co-evolutionary genetic algorithms with different species for different kind (en)
Title
  • Genetic Algorithm with Species for Regularization Network Metalearning
  • Genetic Algorithm with Species for Regularization Network Metalearning (en)
skos:prefLabel
  • Genetic Algorithm with Species for Regularization Network Metalearning
  • Genetic Algorithm with Species for Regularization Network Metalearning (en)
skos:notation
  • RIV/67985807:_____/10:00348394!RIV11-AV0-67985807
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(KJB100300804), Z(AV0Z10300504)
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
  • 260389
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/10:00348394
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • regularization networks; kernel functions; genetic algorithms (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [EEA715594B41]
http://linked.open...v/mistoKonaniAkce
  • Smrekovica
http://linked.open...i/riv/mistoVydani
  • Seňa
http://linked.open...i/riv/nazevZdroje
  • Informačné Technológie - Aplikácie a Teória
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...iv/tvurceVysledku
  • Neruda, Roman
  • Vidnerová, Petra
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
  • Pont
https://schema.org/isbn
  • 978-80-970179-3-4
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