11 Publikationen
2026 | Konferenzbeitrag | FH-PUB-ID: 6898 | 
Jaster B, Tharwat A, Sheikh EM, Kohlhase M, Schenck W. Low Query Budget Active Learning for Classification and Regression. In: Koprinska I, Mendes-Moreira J, Branco P, eds. 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. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2026:5-21. doi:10.1007/978-3-032-19105-2_1
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2026 | Artikel | FH-PUB-ID: 6655 |
Tharwat A, Jaster B, Schenck W, Kohlhase M. Active learning evaluation metrics for classification and regression frameworks. Engineering Applications of Artificial Intelligence. 2026;171. doi:10.1016/j.engappai.2026.114295
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2025 | Konferenzbeitrag | FH-PUB-ID: 6267
Bültemeier J, Holst C-A, Lohweg V, Schöne M, Jaster B, Kohlhase M. AI Workflow for Scarce Data: A Modular Approach to Optimise Processes. In: 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2025:1-4. doi:10.1109/ETFA65518.2025.11205664
HSBI-PUB
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2025 | Buchbeitrag | FH-PUB-ID: 6273 |
Schöne M, Jaster B, Bültemeier J, Kohlhase M. Informed Active Learning with Decision Trees to Balance Exploration and Exploitation. In: Institute for Data Science Solutions, ed. Kongress KI@HSBI2025 Zukunft Im Fokus – Posterbeiträge. Vol 2. Schriftenreihe des Institute for Data Science Solutions. Bielefeld: Hochschule Bielefeld; 2025:26-27. doi:10.60802/sidas.2025.2
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2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Jaster B, Kohlhase M. Trust Issues in Active Learning and Their Impact on Real-World Applications. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion). IEEE; 2025:1-5. doi:10.1109/CITRExCompanion65208.2025.10981492
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2025 | Konferenzbeitrag | FH-PUB-ID: 6045 |
Jaster B, Kohlhase M. Trust Issues in Active Learning and Their Impact on Real-World Applications. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion). IEEE; 2025:1-5. doi:10.57720/6045
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2025 | Konferenzbeitrag | FH-PUB-ID: 6049 |
Schöne M, Jaster B, Bültemeier J, Kösters J, Holst C-A, Kohlhase M. Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx). IEEE; 2025:1-9. doi:10.57720/6049
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2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Schöne M, Jaster B, Bültemeier J, Kösters J, Holst C-A, Kohlhase M. Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx). IEEE; 2025:1-9. doi:10.1109/CITREx64975.2025.10974940
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2024 | Artikel | FH-PUB-ID: 6299 |
Katter V, Jaster B, Schöne M. Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme . Schriftenreihe des Institute for Data Science Solutions. 2024;2. doi:10.60802/SIDAS.2024.2
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2023 | Konferenzbeitrag | FH-PUB-ID: 3713 |
Jaster B, Kohlhase M. Active Learning for Regression Problems with Ensemble Methods. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 33. Workshop Computational Intelligence. Karlsruher Institut für Technologie (KIT); 2023:9-29. doi:10.5445/KSP/1000162754
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2021 | Artikel | FH-PUB-ID: 6498 |
Guss WH, Milani S, Topin N, et al. Towards robust and domain agnostic reinforcement learning competitions. arXiv. 2021. doi:10.48550/ARXIV.2106.03748
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11 Publikationen
2026 | Konferenzbeitrag | FH-PUB-ID: 6898 |
Jaster B, Tharwat A, Sheikh EM, Kohlhase M, Schenck W. Low Query Budget Active Learning for Classification and Regression. In: Koprinska I, Mendes-Moreira J, Branco P, eds. 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. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2026:5-21. doi:10.1007/978-3-032-19105-2_1
HSBI-PUB
| DOI
| Download (ext.)
2026 | Artikel | FH-PUB-ID: 6655 |
Tharwat A, Jaster B, Schenck W, Kohlhase M. Active learning evaluation metrics for classification and regression frameworks. Engineering Applications of Artificial Intelligence. 2026;171. doi:10.1016/j.engappai.2026.114295
HSBI-PUB
| DOI
| Download (ext.)
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
Bültemeier J, Holst C-A, Lohweg V, Schöne M, Jaster B, Kohlhase M. AI Workflow for Scarce Data: A Modular Approach to Optimise Processes. In: 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2025:1-4. doi:10.1109/ETFA65518.2025.11205664
HSBI-PUB
| DOI
2025 | Buchbeitrag | FH-PUB-ID: 6273 |
Schöne M, Jaster B, Bültemeier J, Kohlhase M. Informed Active Learning with Decision Trees to Balance Exploration and Exploitation. In: Institute for Data Science Solutions, ed. Kongress KI@HSBI2025 Zukunft Im Fokus – Posterbeiträge. Vol 2. Schriftenreihe des Institute for Data Science Solutions. Bielefeld: Hochschule Bielefeld; 2025:26-27. doi:10.60802/sidas.2025.2
HSBI-PUB
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2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Jaster B, Kohlhase M. Trust Issues in Active Learning and Their Impact on Real-World Applications. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion). IEEE; 2025:1-5. doi:10.1109/CITRExCompanion65208.2025.10981492
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2025 | Konferenzbeitrag | FH-PUB-ID: 6045 |
Jaster B, Kohlhase M. Trust Issues in Active Learning and Their Impact on Real-World Applications. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion). IEEE; 2025:1-5. doi:10.57720/6045
HSBI-PUB
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| DOI
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 |
Schöne M, Jaster B, Bültemeier J, Kösters J, Holst C-A, Kohlhase M. Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx). IEEE; 2025:1-9. doi:10.57720/6049
HSBI-PUB
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| DOI
2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Schöne M, Jaster B, Bültemeier J, Kösters J, Holst C-A, Kohlhase M. Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit. In: IEEE, ed. 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx). IEEE; 2025:1-9. doi:10.1109/CITREx64975.2025.10974940
HSBI-PUB
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2024 | Artikel | FH-PUB-ID: 6299 |
Katter V, Jaster B, Schöne M. Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme . Schriftenreihe des Institute for Data Science Solutions. 2024;2. doi:10.60802/SIDAS.2024.2
HSBI-PUB
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| Download (ext.)
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 |
Jaster B, Kohlhase M. Active Learning for Regression Problems with Ensemble Methods. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 33. Workshop Computational Intelligence. Karlsruher Institut für Technologie (KIT); 2023:9-29. doi:10.5445/KSP/1000162754
HSBI-PUB
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2021 | Artikel | FH-PUB-ID: 6498 |
Guss WH, Milani S, Topin N, et al. Towards robust and domain agnostic reinforcement learning competitions. arXiv. 2021. doi:10.48550/ARXIV.2106.03748
HSBI-PUB
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| Download (ext.)