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.

  PUB | PDF | DOI
   
• [4]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969235

  Castellani, A., Schmitt, S., and Hammer, B. (2022). “Stream-Based Active Learning with Verification Latency in Non-stationary Environments” in Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV, Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., and Aydin, M. eds. Lecture Notes in Computer Science, vol. 13532, (Cham: Springer Nature Switzerland), 260-272.

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• [3]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982134

  Castellani, A., Schmitt, S., and Hammer, B. (2021). “Task-Sensitive Concept Drift Detector with Constraint Embedding” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE), 01-08.

  PUB | DOI
   
• [2]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969237

  Castellani, A., Schmitt, S., and 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 Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I, Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., and Lozano, J. A. eds. Lecture Notes in Computer Science, vol. 12975, (Cham: Springer International Publishing), 469-484.

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• [1]
  

  2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2969236

  Castellani, A., Schmitt, S., and Squartini, S. (2021). Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning. IEEE Transactions on Industrial Informatics 17, 4733-4742.

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