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pro vyhledávání: '"Kliton Andrea"'
Publikováno v:
Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319089782
MLDM
MLDM
Traditionally, the performance of statistical tests for outlier detection is evaluated by their power and false alarm rate. It requires ensuring the upper bound for false alarm rate while measuring the detection power, which proves to be a difficult
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::18c0af07033c0bc44a5a7b6819ab3115
https://doi.org/10.1007/978-3-319-08979-9_15
https://doi.org/10.1007/978-3-319-08979-9_15
Autor:
Pavel Smirnov, Kliton Andrea, Lakshminarayan Choudur, Alexander Ulanov, Natalia Vassilieva, Georgy Shevlyakov
Publikováno v:
ICASSP
The need for fast on-line algorithms to analyze high data-rate measurements is a vital element in production settings. Given the ever-increasing number of data sources coupled with increasing complexity of applications, and workload patterns, anomaly