Marvin Schöne
11 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | 
J. Bültemeier, M. Schöne, M. Kohlhase, C.-A. Holst, V. Lohweg, and O. Nelles, “Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs,” in Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, Berlin, 2024, pp. 217–231.
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2023 | Diskussionspapier | FH-PUB-ID: 3731 |
J. Kösters and M. Schöne, Active Learning mit dem GUIDE-Entscheidungsbaum. .
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
J. Kösters, M. Schöne, and M. Kohlhase, Benchmarking of Machine Learning Models for Tabular Scarce Data. .
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2022 | Konferenzbeitrag | FH-PUB-ID: 2232
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes,” in Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Manchester, UK, 2022, pp. 379–390.
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2022 | Buchbeitrag | FH-PUB-ID: 2291 |
M. Hanitz, M. Schöne, T. Voigt, and M. Kohlhase, “Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD,” in Machine Learning and Data Mining in Pattern Recognition, MLDM 2022, P. Perner, Ed. Leipzig: ibai-publishing, 2022, pp. 121–135.
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2021 | Konferenzbeitrag | FH-PUB-ID: 1912
M. Schöne and M. Kohlhase, “Curvature-Oriented Splitting for Multivariate Model Trees,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, 2021, pp. 01–09.
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2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
J. Ewerszumrode, M. Schöne, S. Godt, and M. Kohlhase, “Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP,” in Proceedings - 31. Workshop Computational Intelligence , Berlin, 2021, pp. 285–305.
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Space-Filling Designs for Experiments with Assembled Products,” in 2021 3rd International Conference on Management Science and Industrial Engineering, Osaka Japan, 2021, pp. 192–199.
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2020 | Konferenzbeitrag | FH-PUB-ID: 1916
M. Schöne and M. Kohlhase, “Least Squares Approach for Multivariate Split Selection in Regression Trees,” in Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, Guimaraes, Portugal, 2020, pp. 41–50.
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2020 | Buchbeitrag | FH-PUB-ID: 1915 |
M. Schöne and M. Kohlhase, “Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees,” in Proceedings - 30. Workshop Computational Intelligence, H. Schulte, F. Hoffmann, and R. Mikut, Eds. Karlsruhe: KIT Scientific Publishing, 2020.
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11 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 |
J. Bültemeier, M. Schöne, M. Kohlhase, C.-A. Holst, V. Lohweg, and O. Nelles, “Dichte-skaliertes Optimierungskriterium für Sliced Latin Hypercube Designs,” in Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, Berlin, 2024, pp. 217–231.
HSBI-PUB
| DOI
| Download (ext.)
2023 | Diskussionspapier | FH-PUB-ID: 3731 |
J. Kösters and M. Schöne, Active Learning mit dem GUIDE-Entscheidungsbaum. .
HSBI-PUB
| Dateien verfügbar
| Download (ext.)
2023 | Diskussionspapier | FH-PUB-ID: 3729 |
J. Kösters, M. Schöne, and M. Kohlhase, Benchmarking of Machine Learning Models for Tabular Scarce Data. .
HSBI-PUB
| Dateien verfügbar
| Download (ext.)
2022 | Konferenzbeitrag | FH-PUB-ID: 2232
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes,” in Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Manchester, UK, 2022, pp. 379–390.
HSBI-PUB
| DOI
2022 | Buchbeitrag | FH-PUB-ID: 2291 |
M. Hanitz, M. Schöne, T. Voigt, and M. Kohlhase, “Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD,” in Machine Learning and Data Mining in Pattern Recognition, MLDM 2022, P. Perner, Ed. Leipzig: ibai-publishing, 2022, pp. 121–135.
HSBI-PUB
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
M. Schöne and M. Kohlhase, “Curvature-Oriented Splitting for Multivariate Model Trees,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA, 2021, pp. 01–09.
HSBI-PUB
| DOI
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
J. Ewerszumrode, M. Schöne, S. Godt, and M. Kohlhase, “Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP,” in Proceedings - 31. Workshop Computational Intelligence , Berlin, 2021, pp. 285–305.
HSBI-PUB
| DOI
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 3718
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, and M. Kuhn, “Space-Filling Designs for Experiments with Assembled Products,” in 2021 3rd International Conference on Management Science and Industrial Engineering, Osaka Japan, 2021, pp. 192–199.
HSBI-PUB
| DOI
| Download (ext.)
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
M. Schöne and M. Kohlhase, “Least Squares Approach for Multivariate Split Selection in Regression Trees,” in Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, Guimaraes, Portugal, 2020, pp. 41–50.
HSBI-PUB
| DOI
| Download (ext.)
2020 | Buchbeitrag | FH-PUB-ID: 1915 |
M. Schöne and M. Kohlhase, “Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees,” in Proceedings - 30. Workshop Computational Intelligence, H. Schulte, F. Hoffmann, and R. Mikut, Eds. Karlsruhe: KIT Scientific Publishing, 2020.
HSBI-PUB
| DOI
| Download (ext.)