Marvin Schöne
11 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 | 
Bültemeier, Julian, Marvin Schöne, Martin Kohlhase, Christoph-Alexander Holst, Volker Lohweg, and Oliver Nelles. “Dichte-Skaliertes Optimierungskriterium Für Sliced Latin Hypercube Designs.” In Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, edited by Horst Schulte, Frank Hoffmann, and Ralf Mikut, 217–31. KIT Scientific Publishing, 2024. https://doi.org/10.58895/ksp//1000174544-14.
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2023 | Diskussionspapier | FH-PUB-ID: 3731 |
Kösters, Justus, and Marvin Schöne. Active Learning mit dem GUIDE-Entscheidungsbaum, n.d.
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
Kösters, Justus, Marvin Schöne, and Martin Kohlhase. Benchmarking of Machine Learning Models for Tabular Scarce Data, n.d.
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2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, Tim, Marvin Schöne, Martin Kohlhase, Oliver Nelles, and Martin 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, edited by Hujun Yin, David Camacho, and Peter Tino, 379–90. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-21753-1_37.
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2022 | Buchbeitrag | FH-PUB-ID: 2291 |
Hanitz, Marcel, Marvin Schöne, Tim Voigt, and Martin 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, edited by Petra Perner, 121–35. Leipzig: ibai-publishing, 2022.
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2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne, Marvin, and Martin Kohlhase. “Curvature-Oriented Splitting for Multivariate Model Trees.” In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01–09. IEEE, 2021. https://doi.org/10.1109/SSCI50451.2021.9659858.
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2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
Ewerszumrode, Jan, Marvin Schöne, Stephan Godt, and Martin Kohlhase. “Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP.” In Proceedings - 31. Workshop Computational Intelligence , edited by Horst Schulte, Frank Hoffmann, and Ralf Mikut, 285–305. KIT Scientific Publishing, 2021. https://doi.org/10.5445/KSP/1000138532.
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, Tim, Marvin Schöne, Martin Kohlhase, Oliver Nelles, and Martin Kuhn. “Space-Filling Designs for Experiments with Assembled Products.” In 2021 3rd International Conference on Management Science and Industrial Engineering, 192–99. New York, NY, USA: ACM, 2021. https://doi.org/10.1145/3460824.3460854.
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2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, Tim, Nico Migenda, Marvin Schöne, David Pelkmann, Matthias Fricke, Wolfram Schenck, and Martin Kohlhase. “Advanced Data Analytics Platform for Manufacturing Companies.” In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), 01–08. IEEE, 2021. https://doi.org/10.1109/ETFA45728.2021.9613499.
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2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne, Marvin, and Martin 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, edited by Cesar Analide, Paulo Novais, David Camacho, and Hujun Yin, 41–50. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-62362-3_5.
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2020 | Buchbeitrag | FH-PUB-ID: 1915 |
Schöne, Marvin, and Martin Kohlhase. “Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees.” In Proceedings - 30. Workshop Computational Intelligence, edited by Horst Schulte, Frank Hoffmann, and Ralf Mikut. Karlsruhe: KIT Scientific Publishing, 2020. https://doi.org/10.5445/KSP/1000124139.
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11 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5882 |
Bültemeier, Julian, Marvin Schöne, Martin Kohlhase, Christoph-Alexander Holst, Volker Lohweg, and Oliver Nelles. “Dichte-Skaliertes Optimierungskriterium Für Sliced Latin Hypercube Designs.” In Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024, edited by Horst Schulte, Frank Hoffmann, and Ralf Mikut, 217–31. KIT Scientific Publishing, 2024. https://doi.org/10.58895/ksp//1000174544-14.
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2023 | Diskussionspapier | FH-PUB-ID: 3731 |
Kösters, Justus, and Marvin Schöne. Active Learning mit dem GUIDE-Entscheidungsbaum, n.d.
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
Kösters, Justus, Marvin Schöne, and Martin Kohlhase. Benchmarking of Machine Learning Models for Tabular Scarce Data, n.d.
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2022 | Konferenzbeitrag | FH-PUB-ID: 2232
Voigt, Tim, Marvin Schöne, Martin Kohlhase, Oliver Nelles, and Martin 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, edited by Hujun Yin, David Camacho, and Peter Tino, 379–90. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-21753-1_37.
HSBI-PUB
| DOI
2022 | Buchbeitrag | FH-PUB-ID: 2291 |
Hanitz, Marcel, Marvin Schöne, Tim Voigt, and Martin 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, edited by Petra Perner, 121–35. Leipzig: ibai-publishing, 2022.
HSBI-PUB
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 1912
Schöne, Marvin, and Martin Kohlhase. “Curvature-Oriented Splitting for Multivariate Model Trees.” In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01–09. IEEE, 2021. https://doi.org/10.1109/SSCI50451.2021.9659858.
HSBI-PUB
| DOI
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 1560 |
Ewerszumrode, Jan, Marvin Schöne, Stephan Godt, and Martin Kohlhase. “Assistenzsystem zur Qualitätssicherung von IoT-Geräten basierend auf AutoML und SHAP.” In Proceedings - 31. Workshop Computational Intelligence , edited by Horst Schulte, Frank Hoffmann, and Ralf Mikut, 285–305. KIT Scientific Publishing, 2021. https://doi.org/10.5445/KSP/1000138532.
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2021 | Konferenzbeitrag | FH-PUB-ID: 3718
Voigt, Tim, Marvin Schöne, Martin Kohlhase, Oliver Nelles, and Martin Kuhn. “Space-Filling Designs for Experiments with Assembled Products.” In 2021 3rd International Conference on Management Science and Industrial Engineering, 192–99. New York, NY, USA: ACM, 2021. https://doi.org/10.1145/3460824.3460854.
HSBI-PUB
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2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, Tim, Nico Migenda, Marvin Schöne, David Pelkmann, Matthias Fricke, Wolfram Schenck, and Martin Kohlhase. “Advanced Data Analytics Platform for Manufacturing Companies.” In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), 01–08. IEEE, 2021. https://doi.org/10.1109/ETFA45728.2021.9613499.
HSBI-PUB
| DOI
2020 | Konferenzbeitrag | FH-PUB-ID: 1916
Schöne, Marvin, and Martin 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, edited by Cesar Analide, Paulo Novais, David Camacho, and Hujun Yin, 41–50. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-62362-3_5.
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| DOI
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2020 | Buchbeitrag | FH-PUB-ID: 1915 |
Schöne, Marvin, and Martin Kohlhase. “Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees.” In Proceedings - 30. Workshop Computational Intelligence, edited by Horst Schulte, Frank Hoffmann, and Ralf Mikut. Karlsruhe: KIT Scientific Publishing, 2020. https://doi.org/10.5445/KSP/1000124139.
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