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., & Hammer, B., 2022. Stream-Based Active Learning with Verification Latency in Non-stationary Environments. In E. Pimenidis, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV. Lecture Notes in Computer Science. no.13532 Cham: Springer Nature Switzerland, pp. 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, pp. 01-08.

  PUB | DOI
   
• [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 N. Oliver, et al., eds. Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. no.12975 Cham: Springer International Publishing, pp. 469-484.

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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), p 4733-4742.

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