11 Publikationen
2026 | Konferenzbeitrag | FH-PUB-ID: 6898 | 
Jaster, Bjarne, et al. “Low Query Budget Active Learning for Classification and Regression.” Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV, edited by Irena Koprinska et al., Springer Nature Switzerland, 2026, pp. 5–21, doi:10.1007/978-3-032-19105-2_1.
HSBI-PUB
| DOI
| Download (ext.)
2026 | Artikel | FH-PUB-ID: 6655 |
Tharwat, Alaa, et al. “Active Learning Evaluation Metrics for Classification and Regression Frameworks.” Engineering Applications of Artificial Intelligence, vol. 171, 114295, Elsevier BV, 2026, doi:10.1016/j.engappai.2026.114295.
HSBI-PUB
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
Bültemeier, Julian, et al. “AI Workflow for Scarce Data: A Modular Approach to Optimise Processes.” 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2025, pp. 1–4, doi:10.1109/ETFA65518.2025.11205664.
HSBI-PUB
| DOI
2025 | Buchbeitrag | FH-PUB-ID: 6273 |
Schöne, Marvin, et al. “Informed Active Learning with Decision Trees to Balance Exploration and Exploitation.” Kongress KI@HSBI2025 Zukunft Im Fokus – Posterbeiträge, edited by Institute for Data Science Solutions, vol. 2, Hochschule Bielefeld, 2025, pp. 26–27, doi:10.60802/sidas.2025.2.
HSBI-PUB
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Jaster, Bjarne, and Martin Kohlhase. “Trust Issues in Active Learning and Their Impact on Real-World Applications.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), edited by IEEE, IEEE, 2025, pp. 1–5, doi:10.1109/CITRExCompanion65208.2025.10981492.
HSBI-PUB
| Dateien verfügbar
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 |
Jaster, Bjarne, and Martin Kohlhase. “Trust Issues in Active Learning and Their Impact on Real-World Applications.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), edited by IEEE, IEEE, 2025, pp. 1–5, doi:10.57720/6045.
HSBI-PUB
| Dateien verfügbar
| DOI
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 |
Schöne, Marvin, et al. “Pool-Based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), edited by IEEE, IEEE, 2025, pp. 1–9, doi:10.57720/6049.
HSBI-PUB
| Dateien verfügbar
| DOI
2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Schöne, Marvin, et al. “Pool-Based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), edited by IEEE, IEEE, 2025, pp. 1–9, doi:10.1109/CITREx64975.2025.10974940.
HSBI-PUB
| DOI
| Download (ext.)
2024 | Artikel | FH-PUB-ID: 6299 |
Katter, Vincent, et al. “Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme .” Schriftenreihe des Institute for Data Science Solutions, vol. 2, Hochschule Bielefeld, 2024, doi:10.60802/SIDAS.2024.2.
HSBI-PUB
| DOI
| Download (ext.)
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 |
Jaster, Bjarne, and Martin Kohlhase. “Active Learning for Regression Problems with Ensemble Methods.” Proceedings - 33. Workshop Computational Intelligence, edited by Horst Schulte et al., Karlsruher Institut für Technologie (KIT), 2023, pp. 9–29, doi:10.5445/KSP/1000162754.
HSBI-PUB
| DOI
| Download (ext.)
2021 | Artikel | FH-PUB-ID: 6498 |
Guss, William Hebgen, et al. “Towards Robust and Domain Agnostic Reinforcement Learning Competitions.” ArXiv, arXiv, 2021, doi:10.48550/ARXIV.2106.03748.
HSBI-PUB
| DOI
| Download (ext.)
Suche
Publikationen filtern
Darstellung / Sortierung
Export / Einbettung
11 Publikationen
2026 | Konferenzbeitrag | FH-PUB-ID: 6898 |
Jaster, Bjarne, et al. “Low Query Budget Active Learning for Classification and Regression.” Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV, edited by Irena Koprinska et al., Springer Nature Switzerland, 2026, pp. 5–21, doi:10.1007/978-3-032-19105-2_1.
HSBI-PUB
| DOI
| Download (ext.)
2026 | Artikel | FH-PUB-ID: 6655 |
Tharwat, Alaa, et al. “Active Learning Evaluation Metrics for Classification and Regression Frameworks.” Engineering Applications of Artificial Intelligence, vol. 171, 114295, Elsevier BV, 2026, doi:10.1016/j.engappai.2026.114295.
HSBI-PUB
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
Bültemeier, Julian, et al. “AI Workflow for Scarce Data: A Modular Approach to Optimise Processes.” 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2025, pp. 1–4, doi:10.1109/ETFA65518.2025.11205664.
HSBI-PUB
| DOI
2025 | Buchbeitrag | FH-PUB-ID: 6273 |
Schöne, Marvin, et al. “Informed Active Learning with Decision Trees to Balance Exploration and Exploitation.” Kongress KI@HSBI2025 Zukunft Im Fokus – Posterbeiträge, edited by Institute for Data Science Solutions, vol. 2, Hochschule Bielefeld, 2025, pp. 26–27, doi:10.60802/sidas.2025.2.
HSBI-PUB
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Jaster, Bjarne, and Martin Kohlhase. “Trust Issues in Active Learning and Their Impact on Real-World Applications.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), edited by IEEE, IEEE, 2025, pp. 1–5, doi:10.1109/CITRExCompanion65208.2025.10981492.
HSBI-PUB
| Dateien verfügbar
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 |
Jaster, Bjarne, and Martin Kohlhase. “Trust Issues in Active Learning and Their Impact on Real-World Applications.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), edited by IEEE, IEEE, 2025, pp. 1–5, doi:10.57720/6045.
HSBI-PUB
| Dateien verfügbar
| DOI
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 |
Schöne, Marvin, et al. “Pool-Based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), edited by IEEE, IEEE, 2025, pp. 1–9, doi:10.57720/6049.
HSBI-PUB
| Dateien verfügbar
| DOI
2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Schöne, Marvin, et al. “Pool-Based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit.” 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), edited by IEEE, IEEE, 2025, pp. 1–9, doi:10.1109/CITREx64975.2025.10974940.
HSBI-PUB
| DOI
| Download (ext.)
2024 | Artikel | FH-PUB-ID: 6299 |
Katter, Vincent, et al. “Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme .” Schriftenreihe des Institute for Data Science Solutions, vol. 2, Hochschule Bielefeld, 2024, doi:10.60802/SIDAS.2024.2.
HSBI-PUB
| DOI
| Download (ext.)
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 |
Jaster, Bjarne, and Martin Kohlhase. “Active Learning for Regression Problems with Ensemble Methods.” Proceedings - 33. Workshop Computational Intelligence, edited by Horst Schulte et al., Karlsruher Institut für Technologie (KIT), 2023, pp. 9–29, doi:10.5445/KSP/1000162754.
HSBI-PUB
| DOI
| Download (ext.)
2021 | Artikel | FH-PUB-ID: 6498 |
Guss, William Hebgen, et al. “Towards Robust and Domain Agnostic Reinforcement Learning Competitions.” ArXiv, arXiv, 2021, doi:10.48550/ARXIV.2106.03748.
HSBI-PUB
| DOI
| Download (ext.)