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1 Ergebnisse
1
Detecting Spacecraft Anomalies Using LSTMs and Nonparametri..:
, In:
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
,
Hundman, Kyle
;
Constantinou, Valentino
;
Laporte, Christopher
.. - p. 387-395 , 2018
Link:
https://dl.acm.org/doi/10.1145/3219819.3219845
RT T1 <cTitle>Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</cTitle>: T1 <Title>Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3219845&Exemplar=1&LAN=DE A1 Hundman, Kyle A1 Constantinou, Valentino A1 Laporte, Christopher A1 Colwell, Ian A1 Soderstrom, Tom PB ACM YR 2018 K1 aerospace K1 anomaly detection K1 forecasting K1 lstms K1 neural networks K1 rnns K1 time-series K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Unsupervised learning K1 Anomaly detection K1 Applied computing K1 Operations research K1 Forecasting K1 Machine learning approaches K1 Neural networks K1 Learning settings K1 Semi-supervised learning settings SP 387 OP 395 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3219819.3219845 DO https://dl.acm.org/doi/10.1145/3219819.3219845 SF ELIB - SuUB Bremen
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