Detecting unregistered users through semi-supervised anomaly detection with similarity datasets

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource, 1 online resource.

Language: Englisch

Bibliographic citation: Detecting unregistered users through semi-supervised anomaly detection with similarity datasets ; volume:10 ; number:1 ; day:12 ; month:6 ; year:2023 ; pages:1-22 ; date:12.2023
Journal of Big Data ; 10, Heft 1 (12.6.2023), 1-22, 12.2023

Creator: Heo, Dong Hyuk
Park, Sung Ho
Kang, Soon Ju

Contributor: SpringerLink (Online service)

DOI: 10.1186/s40537-023-00791-8

URN: urn:nbn:de:101:1-2023091913590824836501

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:54 AM CEST

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Heo, Dong Hyuk
Park, Sung Ho
Kang, Soon Ju
SpringerLink (Online service)

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Detecting unregistered users through semi-supervised anomaly detection with similarity datasets

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