PUB - Publikationen an der Universität Bielefeld

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• [5]
  

  2024 | Bielefelder E-Dissertation | PUB-ID: 2985975

  Castellani, A. (2024). Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data. Bielefeld: Universität Bielefeld. doi:10.4119/unibi/2985975.

  PUB | PDF | DOI
   
• [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 (Lecture Notes in Computer Science). In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas & M. 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 (S. 260-272). Gehalten auf der International Conference on Artificial Neural Networks (ICANN 2022), Cham: Springer Nature Switzerland. doi:10.1007/978-3-031-15937-4_22.

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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. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (S. 01-08). Gehalten auf der 2021 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE. doi:10.1109/SSCI50451.2021.9659969.

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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 (Lecture Notes in Computer Science). In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read & J.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 (S. 469-484). Gehalten auf der ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Cham: Springer International Publishing. doi:10.1007/978-3-030-86486-6_29.

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• [1]
  

  2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2969236

  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. Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/TII.2020.3019788.

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