Andrea Castellani
acastellani@techfak.uni-bielefeld.dehttps://orcid.org/0000-0003-0476-5978
PEVZ-ID
5 Publikationen
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2024 | Bielefelder E-Dissertation | PUB-ID: 2985975Castellani, A. (2024). Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data. Bielefeld: Universität Bielefeld. https://doi.org/10.4119/unibi/2985975PUB | PDF | DOI
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969235Castellani, A., Schmitt, S., & Hammer, B. (2022). Stream-Based Active Learning with Verification Latency in Non-stationary Environments. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Lecture Notes in Computer Science: Vol. 13532. Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (pp. 260-272). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-15937-4_22PUB | DOI | Download (ext.)
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982134Castellani, A., Schmitt, S., & Hammer, B. (2021). Task-Sensitive Concept Drift Detector with Constraint Embedding. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-08. IEEE. https://doi.org/10.1109/SSCI50451.2021.9659969PUB | DOI
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2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969237Castellani, 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, F. Pérez-Cruz, S. Kramer, J. Read, & J. A. Lozano (Eds.), Lecture Notes in Computer Science: Vol. 12975. Machine Learning and Knowledge Discovery in Databases. Research Track. European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I (pp. 469-484). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-86486-6_29PUB | DOI | Download (ext.)
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2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2969236Castellani, 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. https://doi.org/10.1109/TII.2020.3019788PUB | PDF | DOI | Download (ext.) | WoS