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Marvin Schöne

18 Publikationen

Alle markieren

[18]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
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 | DOI | Download (ext.)
 
[17]
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
 
[16]
2025 | Artikel | FH-PUB-ID: 6268 | OA
Schöne M, Kohlhase M, Nelles O. Incorporation of structural properties of the response surface into oblique model trees. at - Automatisierungstechnik. 2025;73(10):727-739. doi:10.1515/auto-2025-0017
HSBI-PUB | DOI | Download (ext.)
 
[15]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
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 | Dateien verfügbar | DOI
 
[14]
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 | DOI | Download (ext.)
 
[13]
2024 | Artikel | FH-PUB-ID: 6299 | OA
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 | DOI | Download (ext.)
 
[12]
2024 | Buchbeitrag | FH-PUB-ID: 6272 | OA
Schöne M, Bültemeier J. AI for Scarce Data (AI4ScaDa) — Maschinelles Lernen und Informationsfusion zur nachhaltigen Nutzung von Labor- und Kundendaten. In: Institute for Data Science Solutions, ed. Kongress KI@HSBI2023 Solutions Im Fokus – Posterbeiträge. Vol 1. Schriftenreihe des Institute for Data Science Solutions. Bielefeld: Hochschule Bielefeld; 2024:28-29. doi:10.60802/sidas.2024.1
HSBI-PUB | DOI | Download (ext.)
 
[11]
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | OA
Bültemeier J, Schöne M, Kohlhase M, Holst C-A, Lohweg V, Nelles O. Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024. KIT Scientific Publishing; 2024:217-231. doi:10.58895/ksp//1000174544-14
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Diskussionspapier | FH-PUB-ID: 3731 | OA
Kösters J, Schöne M. Active Learning mit dem GUIDE-Entscheidungsbaum.
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[9]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
Kösters J, Schöne M, Kohlhase M. Benchmarking of Machine Learning Models for Tabular Scarce Data.
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[8]
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In: Yin H, Camacho D, Tino P, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2022:379-390. doi:10.1007/978-3-031-21753-1_37
HSBI-PUB | DOI
 
[7]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
Hanitz M, Schöne M, Voigt T, Kohlhase M. Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In: Perner P, ed. Machine Learning and Data Mining in Pattern Recognition, MLDM 2022. Leipzig: ibai-publishing; 2022:121-135.
HSBI-PUB | Download (ext.)
 
[6]
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne M, Kohlhase M. Curvature-Oriented Splitting for Multivariate Model Trees. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2021:01-09. doi:10.1109/SSCI50451.2021.9659858
HSBI-PUB | DOI | Download (ext.)
 
[5]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
Ewerszumrode J, Schöne M, Godt S, Kohlhase M. Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 31. Workshop Computational Intelligence . KIT Scientific Publishing; 2021:285-305. doi:10.5445/KSP/1000138532
HSBI-PUB | DOI | Download (ext.)
 
[4]
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Space-Filling Designs for Experiments with Assembled Products. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:192-199. doi:10.1145/3460824.3460854
HSBI-PUB | DOI | Download (ext.)
 
[3]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi:10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[2]
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne M, Kohlhase M. Least Squares Approach for Multivariate Split Selection in Regression Trees. In: Analide C, Novais P, Camacho D, Yin H, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2020:41-50. doi:10.1007/978-3-030-62362-3_5
HSBI-PUB | DOI | Download (ext.)
 
[1]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
Schöne M, Kohlhase M. Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2020. doi:10.5445/KSP/1000124139
HSBI-PUB | DOI | Download (ext.)
 
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18 Publikationen

Alle markieren

[18]
2025 | Buchbeitrag | FH-PUB-ID: 6273 | OA
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 | DOI | Download (ext.)
 
[17]
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
 
[16]
2025 | Artikel | FH-PUB-ID: 6268 | OA
Schöne M, Kohlhase M, Nelles O. Incorporation of structural properties of the response surface into oblique model trees. at - Automatisierungstechnik. 2025;73(10):727-739. doi:10.1515/auto-2025-0017
HSBI-PUB | DOI | Download (ext.)
 
[15]
2025 | Konferenzbeitrag | FH-PUB-ID: 6049 | OA
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 | Dateien verfügbar | DOI
 
[14]
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 | DOI | Download (ext.)
 
[13]
2024 | Artikel | FH-PUB-ID: 6299 | OA
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 | DOI | Download (ext.)
 
[12]
2024 | Buchbeitrag | FH-PUB-ID: 6272 | OA
Schöne M, Bültemeier J. AI for Scarce Data (AI4ScaDa) — Maschinelles Lernen und Informationsfusion zur nachhaltigen Nutzung von Labor- und Kundendaten. In: Institute for Data Science Solutions, ed. Kongress KI@HSBI2023 Solutions Im Fokus – Posterbeiträge. Vol 1. Schriftenreihe des Institute for Data Science Solutions. Bielefeld: Hochschule Bielefeld; 2024:28-29. doi:10.60802/sidas.2024.1
HSBI-PUB | DOI | Download (ext.)
 
[11]
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | OA
Bültemeier J, Schöne M, Kohlhase M, Holst C-A, Lohweg V, Nelles O. Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024. KIT Scientific Publishing; 2024:217-231. doi:10.58895/ksp//1000174544-14
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Diskussionspapier | FH-PUB-ID: 3731 | OA
Kösters J, Schöne M. Active Learning mit dem GUIDE-Entscheidungsbaum.
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[9]
2023 | Diskussionspapier | FH-PUB-ID: 3729 | OA
Kösters J, Schöne M, Kohlhase M. Benchmarking of Machine Learning Models for Tabular Scarce Data.
HSBI-PUB | Dateien verfügbar | Download (ext.)
 
[8]
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In: Yin H, Camacho D, Tino P, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2022:379-390. doi:10.1007/978-3-031-21753-1_37
HSBI-PUB | DOI
 
[7]
2022 | Buchbeitrag | FH-PUB-ID: 2291 | OA
Hanitz M, Schöne M, Voigt T, Kohlhase M. Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In: Perner P, ed. Machine Learning and Data Mining in Pattern Recognition, MLDM 2022. Leipzig: ibai-publishing; 2022:121-135.
HSBI-PUB | Download (ext.)
 
[6]
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne M, Kohlhase M. Curvature-Oriented Splitting for Multivariate Model Trees. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2021:01-09. doi:10.1109/SSCI50451.2021.9659858
HSBI-PUB | DOI | Download (ext.)
 
[5]
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 | OA
Ewerszumrode J, Schöne M, Godt S, Kohlhase M. Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 31. Workshop Computational Intelligence . KIT Scientific Publishing; 2021:285-305. doi:10.5445/KSP/1000138532
HSBI-PUB | DOI | Download (ext.)
 
[4]
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt T, Schöne M, Kohlhase M, Nelles O, Kuhn M. Space-Filling Designs for Experiments with Assembled Products. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:192-199. doi:10.1145/3460824.3460854
HSBI-PUB | DOI | Download (ext.)
 
[3]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi:10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[2]
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne M, Kohlhase M. Least Squares Approach for Multivariate Split Selection in Regression Trees. In: Analide C, Novais P, Camacho D, Yin H, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2020:41-50. doi:10.1007/978-3-030-62362-3_5
HSBI-PUB | DOI | Download (ext.)
 
[1]
2020 | Buchbeitrag | FH-PUB-ID: 1915 | OA
Schöne M, Kohlhase M. Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In: Schulte H, Hoffmann F, Mikut R, eds. Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing; 2020. doi:10.5445/KSP/1000124139
HSBI-PUB | DOI | Download (ext.)
 
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Suche

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Darstellung / Sortierung

Zitationsstil: AMA

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