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Description
  • We present the method of Gaussian Process Regression (GPR) and discuss the issues of training and prediction and their efficient realization. Further, we consider the application of GPR in design optimization and modeling experimental data.
  • We present the method of Gaussian Process Regression (GPR) and discuss the issues of training and prediction and their efficient realization. Further, we consider the application of GPR in design optimization and modeling experimental data. (en)
  • Představíme metodu Gaussian Process Regression a diskutujeme problémy trénování, predikce a jejich efektivní realizaci. Dále se zabýváme aplikací GPR v návrhové optimalizaci a modelování experimentálních dat. (cs)
Title
  • Use of Gaussian Process Regression in Processing Scientific Data
  • Use of Gaussian Process Regression in Processing Scientific Data (en)
  • Použití Gaussian Process Regression pro Zpracování Vědeckých Dat (cs)
skos:prefLabel
  • Use of Gaussian Process Regression in Processing Scientific Data
  • Use of Gaussian Process Regression in Processing Scientific Data (en)
  • Použití Gaussian Process Regression pro Zpracování Vědeckých Dat (cs)
skos:notation
  • RIV/00010669:_____/09:#0000876!RIV09-MSM-00010669
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  • Z(MSM0001066901)
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  • 347883
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  • RIV/00010669:_____/09:#0000876
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  • data processing; regression; gaussian process (en)
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  • [7BFF91299431]
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  • Praha
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  • Praha
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  • WDS'08 Proceedings
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  • Hájek, Jaroslav
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number of pages
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  • Matematicko-Fyzikální Fakulta Univerzity Karlovy
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  • 978-80-7378-065-4
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