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pro vyhledávání: '"Myrtakis, Nikolaos"'
Autor:
Ntroumpogiannis, Antonios, Giannoulis, Michail, Myrtakis, Nikolaos, Christophides, Vassilis, Simon, Eric, Tsamardinos, Ioannis
Real-time detection of anomalies in streaming data is receiving increasing attention as it allows us to raise alerts, predict faults, and detect intrusions or threats across industries. Yet, little attention has been given to compare the effectivenes
Externí odkaz:
http://arxiv.org/abs/2209.05899
Numerous algorithms have been proposed for detecting anomalies (outliers, novelties) in an unsupervised manner. Unfortunately, it is not trivial, in general, to understand why a given sample (record) is labelled as an anomaly and thus diagnose its ro
Externí odkaz:
http://arxiv.org/abs/2110.09467
Autor:
Ntroumpogiannis, Antonios, Giannoulis, Michail, Myrtakis, Nikolaos, Christophides, Vassilis, Simon, Eric, Tsamardinos, Ioannis
Publikováno v:
VLDB Journal International Journal on Very Large Data Bases; Jul2023, Vol. 32 Issue 4, p845-886, 42p
Numerous algorithms have been proposed for detecting anomalies (outliers, novelties) in an unsupervised manner. Unfortunately, it is not trivial, in general, to understand why a given sample (record) is labelled as an anomaly and thus diagnose its ro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16c8115244b0a71d4e5cfafb49a2ddec
https://hal.science/hal-03608625
https://hal.science/hal-03608625