In Theory and Practice - On the Rate of Convergence of Implementable Neural Network Regression Estimates

In Theory and Practice - On the Rate of Convergence of Implementable Neural Network Regression Estimates

In theory, recent results in nonparametric regression show that neural network estimates are able to achieve good rates of convergence provided suitable assumptions on the structure of the regression function are imposed. However, these theoretical analyses cannot explain the practical success of ne...

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Bibliographic Details
Main Author: Braun, Alina
Format: Others
Language:en
Published: 2021
Online Access:https://tuprints.ulb.tu-darmstadt.de/19052/1/Dissertation_BraunAlina_genehmigt.pdf
Braun, Alina <http://tuprints.ulb.tu-darmstadt.de/view/person/Braun=3AAlina=3A=3A.html> (2021):In Theory and Practice - On the Rate of Convergence of Implementable Neural Network Regression Estimates. (Publisher's Version)Darmstadt, Technische Universität, DOI: 10.26083/tuprints-00019052 <https://doi.org/10.26083/tuprints-00019052>, [Ph.D. Thesis]

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https://tuprints.ulb.tu-darmstadt.de/19052/1/Dissertation_BraunAlina_genehmigt.pdf
Braun, Alina <http://tuprints.ulb.tu-darmstadt.de/view/person/Braun=3AAlina=3A=3A.html> (2021):In Theory and Practice - On the Rate of Convergence of Implementable Neural Network Regression Estimates. (Publisher's Version)Darmstadt, Technische Universität, DOI: 10.26083/tuprints-00019052 <https://doi.org/10.26083/tuprints-00019052>, [Ph.D. Thesis]

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