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1 Ergebnisse
1
Deep Spatio-Temporal Multiple Domain Fusion Network for Urb..:
, In:
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
,
Liu, Ruiqiang
;
Zhao, Shuai
;
Cheng, Bo
... - p. 905-914 , 2020
Link:
https://dl.acm.org/doi/10.1145/3340531.3411920
RT T1 <cTitle>Proceedings of the 29th ACM International Conference on Information & Knowledge Management</cTitle>: T1 <Title>Deep Spatio-Temporal Multiple Domain Fusion Network for Urban Anomalies Detection</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3411920&Exemplar=1&LAN=DE A1 Liu, Ruiqiang A1 Zhao, Shuai A1 Cheng, Bo A1 Yang, Hao A1 Tang, Haina A1 Li, Taoyu PB ACM YR 2020 K1 anomaly detection K1 multiple domain fusion K1 spatiotemporal data K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Learning paradigms K1 Supervised learning K1 Supervised learning by classification SP 905 OP 914 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3340531.3411920 DO https://dl.acm.org/doi/10.1145/3340531.3411920 SF ELIB - SuUB Bremen
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