5 Publikationen

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  • [5]
    2024 | Bielefelder E-Dissertation | PUB-ID: 2985975 OA
    Castellani, A. (2024): Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data. Bielefeld: Universität Bielefeld.
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  • [4]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969235
    Castellani, A.; Schmitt, S.; Hammer, B. (2022): Stream-Based Active Learning with Verification Latency in Non-stationary Environments. In: Elias Pimenidis; Plamen Angelov; Chrisina Jayne; Antonios Papaleonidas; Mehmet Aydin (Hrsg.): Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Cham: Springer Nature Switzerland. (Lecture Notes in Computer Science, 13532). S. 260-272.
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  • [3]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982134
    Castellani, A.; Schmitt, S.; Hammer, B. (2021): Task-Sensitive Concept Drift Detector with Constraint Embedding. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. S. 01-08.
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  • [2]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969237
    Castellani, A.; Schmitt, S.; Hammer, B. (2021): Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise. In: Nuria Oliver; Fernando Pérez-Cruz; Stefan Kramer; Jesse Read; Jose A. Lozano (Hrsg.): Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I. Cham: Springer International Publishing. (Lecture Notes in Computer Science, 12975). S. 469-484.
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  • [1]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2969236 OA
    Castellani, A.; Schmitt, S.; Squartini, S. (2021): Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning IEEE Transactions on Industrial Informatics,17:(7): 4733-4742.
    PUB | PDF | DOI | Download (ext.) | WoS
     

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