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9 Publikationen

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[9]
2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), IEEE, 2025, pp. 1–9.
HSBI-PUB | DOI | Download (ext.)
 
[8]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), IEEE, 2025, pp. 1–9.
HSBI-PUB | Dateien verfügbar | DOI
 
[7]
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Trust Issues in Active Learning and Their Impact on Real-World Applications
B. Jaster, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), IEEE, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[6]
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 | OA
Trust Issues in Active Learning and Their Impact on Real-World Applications
B. Jaster, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), IEEE, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI
 
[5]
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
AI Workflow for Scarce Data: A Modular Approach to Optimise Processes
J. Bültemeier, C.-A. Holst, V. Lohweg, M. Schöne, B. Jaster, M. Kohlhase, in: 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2025, pp. 1–4.
HSBI-PUB | DOI
 
[4]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
Informed Active Learning with Decision Trees to Balance Exploration and Exploitation
M. Schöne, B. Jaster, J. Bültemeier, M. Kohlhase, in: Institute for Data Science Solutions (Ed.), Kongress KI@HSBI2025 Zukunft Im Fokus – Posterbeiträge, Hochschule Bielefeld, Bielefeld, 2025, pp. 26–27.
HSBI-PUB | DOI | Download (ext.)
 
[3]
2024 | Artikel | FH-PUB-ID: 6299 | OA
Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme
V. Katter, B. Jaster, M. Schöne, Schriftenreihe des Institute for Data Science Solutions 2 (2024).
HSBI-PUB | DOI | Download (ext.)
 
[2]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
Active Learning for Regression Problems with Ensemble Methods
B. Jaster, M. Kohlhase, in: H. Schulte, F. Hoffmann, R. Mikut (Eds.), Proceedings - 33. Workshop Computational Intelligence, Karlsruher Institut für Technologie (KIT), 2023, pp. 9–29.
HSBI-PUB | DOI | Download (ext.)
 
[1]
2021 | Artikel | FH-PUB-ID: 6498 | OA
Towards robust and domain agnostic reinforcement learning competitions
W.H. Guss, S. Milani, N. Topin, B. Houghton, S. Mohanty, A. Melnik, A. Harter, B. Buschmaas, B. Jaster, C. Berganski, D. Heitkamp, M. Henning, H. Ritter, C. Wu, X. Hao, Y. Lu, H. Mao, Y. Mao, C. Wang, M. Opanowicz, A. Kanervisto, Y. Schraner, C. Scheller, X. Zhou, L. Liu, D. Nishio, T. Tsuneda, K. Ramanauskas, G. Juceviciute, ArXiv (2021).
HSBI-PUB | DOI | Download (ext.)
 

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9 Publikationen

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[9]
2025 | Konferenzbeitrag | FH-PUB-ID: 5904
Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), IEEE, 2025, pp. 1–9.
HSBI-PUB | DOI | Download (ext.)
 
[8]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit
M. Schöne, B. Jaster, J. Bültemeier, J. Kösters, C.-A. Holst, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx), IEEE, 2025, pp. 1–9.
HSBI-PUB | Dateien verfügbar | DOI
 
[7]
2025 | Konferenzbeitrag | FH-PUB-ID: 5905
Trust Issues in Active Learning and Their Impact on Real-World Applications
B. Jaster, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), IEEE, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[6]
2025 | Konferenzbeitrag | FH-PUB-ID: 6045 | OA
Trust Issues in Active Learning and Their Impact on Real-World Applications
B. Jaster, M. Kohlhase, in: IEEE (Ed.), 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion), IEEE, 2025, pp. 1–5.
HSBI-PUB | Dateien verfügbar | DOI
 
[5]
2025 | Konferenzbeitrag | FH-PUB-ID: 6267
AI Workflow for Scarce Data: A Modular Approach to Optimise Processes
J. Bültemeier, C.-A. Holst, V. Lohweg, M. Schöne, B. Jaster, M. Kohlhase, in: 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2025, pp. 1–4.
HSBI-PUB | DOI
 
[4]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
Informed Active Learning with Decision Trees to Balance Exploration and Exploitation
M. Schöne, B. Jaster, J. Bültemeier, M. Kohlhase, in: Institute for Data Science Solutions (Ed.), Kongress KI@HSBI2025 Zukunft Im Fokus – Posterbeiträge, Hochschule Bielefeld, Bielefeld, 2025, pp. 26–27.
HSBI-PUB | DOI | Download (ext.)
 
[3]
2024 | Artikel | FH-PUB-ID: 6299 | OA
Evaluierung der Leistungsfähigkeit von LSTM-Modellen für die Approximation physikalischer Systeme
V. Katter, B. Jaster, M. Schöne, Schriftenreihe des Institute for Data Science Solutions 2 (2024).
HSBI-PUB | DOI | Download (ext.)
 
[2]
2023 | Konferenzbeitrag | FH-PUB-ID: 3713 | OA
Active Learning for Regression Problems with Ensemble Methods
B. Jaster, M. Kohlhase, in: H. Schulte, F. Hoffmann, R. Mikut (Eds.), Proceedings - 33. Workshop Computational Intelligence, Karlsruher Institut für Technologie (KIT), 2023, pp. 9–29.
HSBI-PUB | DOI | Download (ext.)
 
[1]
2021 | Artikel | FH-PUB-ID: 6498 | OA
Towards robust and domain agnostic reinforcement learning competitions
W.H. Guss, S. Milani, N. Topin, B. Houghton, S. Mohanty, A. Melnik, A. Harter, B. Buschmaas, B. Jaster, C. Berganski, D. Heitkamp, M. Henning, H. Ritter, C. Wu, X. Hao, Y. Lu, H. Mao, Y. Mao, C. Wang, M. Opanowicz, A. Kanervisto, Y. Schraner, C. Scheller, X. Zhou, L. Liu, D. Nishio, T. Tsuneda, K. Ramanauskas, G. Juceviciute, ArXiv (2021).
HSBI-PUB | DOI | Download (ext.)
 

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