Best k-Layer Neural Network Approximations

dc.contributor.authorLim, Lek-Heng
dc.contributor.authorMichalek, Mateusz
dc.contributor.authorQi, Yang
dc.date.accessioned2021-06-23T09:51:38Z
dc.date.available2021-06-23T09:51:38Z
dc.date.issued2022eng
dc.description.versionpublishedde
dc.identifier.doi10.1007/s00365-021-09545-2eng
dc.identifier.urihttps://kops.uni-konstanz.de/handle/123456789/54080
dc.language.isoengeng
dc.subject.ddc510eng
dc.titleBest k-Layer Neural Network Approximationseng
dc.typeJOURNAL_ARTICLEde
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@article{Lim2022kLaye-54080,
  year={2022},
  doi={10.1007/s00365-021-09545-2},
  title={Best k-Layer Neural Network Approximations},
  number={1},
  volume={55},
  issn={0176-4276},
  journal={Constructive Approximation},
  pages={583--604},
  author={Lim, Lek-Heng and Michalek, Mateusz and Qi, Yang}
}
kops.citation.iso690LIM, Lek-Heng, Mateusz MICHALEK, Yang QI, 2022. Best k-Layer Neural Network Approximations. In: Constructive Approximation. Springer. 2022, 55(1), pp. 583-604. ISSN 0176-4276. eISSN 1432-0940. Available under: doi: 10.1007/s00365-021-09545-2deu
kops.citation.iso690LIM, Lek-Heng, Mateusz MICHALEK, Yang QI, 2022. Best k-Layer Neural Network Approximations. In: Constructive Approximation. Springer. 2022, 55(1), pp. 583-604. ISSN 0176-4276. eISSN 1432-0940. Available under: doi: 10.1007/s00365-021-09545-2eng
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kops.sourcefieldConstructive Approximation. Springer. 2022, <b>55</b>(1), pp. 583-604. ISSN 0176-4276. eISSN 1432-0940. Available under: doi: 10.1007/s00365-021-09545-2deu
kops.sourcefield.plainConstructive Approximation. Springer. 2022, 55(1), pp. 583-604. ISSN 0176-4276. eISSN 1432-0940. Available under: doi: 10.1007/s00365-021-09545-2deu
kops.sourcefield.plainConstructive Approximation. Springer. 2022, 55(1), pp. 583-604. ISSN 0176-4276. eISSN 1432-0940. Available under: doi: 10.1007/s00365-021-09545-2eng
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source.periodicalTitleConstructive Approximationeng
source.publisherSpringereng

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