Marvin Schöne
11 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | 
Bültemeier, J., Schöne, M., Kohlhase, M., Holst, C.-A., Lohweg, V., & Nelles, O. (2024). Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024 (pp. 217–231). Berlin: KIT Scientific Publishing. https://doi.org/10.58895/ksp//1000174544-14
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2023 | Diskussionspapier | FH-PUB-ID: 3731 |
Kösters, J., & Schöne, M. (n.d.). Active Learning mit dem GUIDE-Entscheidungsbaum.
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
Kösters, J., Schöne, M., & Kohlhase, M. (n.d.). Benchmarking of Machine Learning Models for Tabular Scarce Data.
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2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2022). Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 379–390). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_37
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2022 | Buchbeitrag | FH-PUB-ID: 2291 |
Hanitz, M., Schöne, M., Voigt, T., & Kohlhase, M. (2022). Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022 (pp. 121–135). Leipzig: ibai-publishing.
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2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne, M., & Kohlhase, M. (2021). Curvature-Oriented Splitting for Multivariate Model Trees. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01–09). Orlando, FL, USA: IEEE. https://doi.org/10.1109/SSCI50451.2021.9659858
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2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
Ewerszumrode, J., Schöne, M., Godt, S., & Kohlhase, M. (2021). Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 31. Workshop Computational Intelligence (pp. 285–305). Berlin: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000138532
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2021). Space-Filling Designs for Experiments with Assembled Products. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 192–199). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460854
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2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
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2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne, M., & Kohlhase, M. (2020). Least Squares Approach for Multivariate Split Selection in Regression Trees. In C. Analide, P. Novais, D. Camacho, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I (pp. 41–50). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-62362-3_5
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2020 | Buchbeitrag | FH-PUB-ID: 1915 |
Schöne, M., & Kohlhase, M. (2020). Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000124139
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11 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 |
Bültemeier, J., Schöne, M., Kohlhase, M., Holst, C.-A., Lohweg, V., & Nelles, O. (2024). Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024 (pp. 217–231). Berlin: KIT Scientific Publishing. https://doi.org/10.58895/ksp//1000174544-14
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2023 | Diskussionspapier | FH-PUB-ID: 3731 |
Kösters, J., & Schöne, M. (n.d.). Active Learning mit dem GUIDE-Entscheidungsbaum.
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
Kösters, J., Schöne, M., & Kohlhase, M. (n.d.). Benchmarking of Machine Learning Models for Tabular Scarce Data.
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2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2022). Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 379–390). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_37
HSBI-PUB
| DOI
2022 | Buchbeitrag | FH-PUB-ID: 2291 |
Hanitz, M., Schöne, M., Voigt, T., & Kohlhase, M. (2022). Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022 (pp. 121–135). Leipzig: ibai-publishing.
HSBI-PUB
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne, M., & Kohlhase, M. (2021). Curvature-Oriented Splitting for Multivariate Model Trees. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01–09). Orlando, FL, USA: IEEE. https://doi.org/10.1109/SSCI50451.2021.9659858
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2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
Ewerszumrode, J., Schöne, M., Godt, S., & Kohlhase, M. (2021). Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 31. Workshop Computational Intelligence (pp. 285–305). Berlin: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000138532
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, T., Schöne, M., Kohlhase, M., Nelles, O., & Kuhn, M. (2021). Space-Filling Designs for Experiments with Assembled Products. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 192–199). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460854
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2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
HSBI-PUB
| DOI
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne, M., & Kohlhase, M. (2020). Least Squares Approach for Multivariate Split Selection in Regression Trees. In C. Analide, P. Novais, D. Camacho, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I (pp. 41–50). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-62362-3_5
HSBI-PUB
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2020 | Buchbeitrag | FH-PUB-ID: 1915 |
Schöne, M., & Kohlhase, M. (2020). Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In H. Schulte, F. Hoffmann, & R. Mikut (Eds.), Proceedings - 30. Workshop Computational Intelligence. Karlsruhe: KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000124139
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