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Description
  • Schopnost učení neuronových sítí na základě příkladů může být modelována pomocí regularizace, která byla vyvinuta jako nástroj pro zlepšení stability řešení inverzních úloh. (cs)
  • Capability of generalization in learning of neural networks from examples can be modelled using regularization, which has been developed as a tool for improving stability of solutions of inverse problems. Such problems are typically described by integral operators. It is shown that learning from examples can be reformulated as an inverse problem defined by an evaluation operator. This reformulation leads to an analytical description of an optimal input/output function of a network with kernel units, which can be employed to design a learning algorithm based on a numerical solution of a system of linear equations.
  • Capability of generalization in learning of neural networks from examples can be modelled using regularization, which has been developed as a tool for improving stability of solutions of inverse problems. Such problems are typically described by integral operators. It is shown that learning from examples can be reformulated as an inverse problem defined by an evaluation operator. This reformulation leads to an analytical description of an optimal input/output function of a network with kernel units, which can be employed to design a learning algorithm based on a numerical solution of a system of linear equations. (en)
Title
  • Supervised Learning with Generalization as an Inverse Problem
  • Supervised Learning with Generalization as an Inverse Problem (en)
  • Učení neuronových sítí jako inverzní úloha (cs)
skos:prefLabel
  • Supervised Learning with Generalization as an Inverse Problem
  • Supervised Learning with Generalization as an Inverse Problem (en)
  • Učení neuronových sítí jako inverzní úloha (cs)
skos:notation
  • RIV/67985807:_____/05:00405483!RIV06-AV0-67985807
http://linked.open.../vavai/riv/strany
  • 551;559
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET100300517), Z(AV0Z10300504)
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
  • 545439
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/05:00405483
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • learning from data; generalization; empirical error functional; inverse problem; evaluation operator; kernel methods (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV
  • [7584CC6BB97C]
http://linked.open...i/riv/nazevZdroje
  • Interest Group in Pure and Applied Logics. Logic Journal
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
  • 13
http://linked.open...iv/tvurceVysledku
  • Kůrková, Věra
http://linked.open...n/vavai/riv/zamer
issn
  • 1367-0751
number of pages
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