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184 Publications
2018 | Conference Paper | LibreCat-ID: 3852
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
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2018 | Conference Paper | LibreCat-ID: 2479
F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018.
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2018 | Conference Paper | LibreCat-ID: 10153
F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-class Classification,” in Proc. 17th Int. Symposium on Intelligent Data Analysis (IDA), 2018, pp. 225–237.
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2018 | Bachelorsthesis | LibreCat-ID: 5936
M. Scheibl, Learning about learning curves from dataset properties. 2018.
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2018 | Conference Paper | LibreCat-ID: 10185
N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M. D. Wever, and E. Hüllermeier, “Supporting the Cognitive Process in Annotation Tasks,” in Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, 2018.
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2018 | Conference Paper | LibreCat-ID: 10154
F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in Proc. 15th Int. Conference on Services Computing (SCC), 2018, pp. 241–244.
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2018 | Conference Paper | LibreCat-ID: 10192
M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in Int. Workshop on Automatic Machine Learning (AutoML) at ICML 2018, 2018.
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2018 | Journal Article | LibreCat-ID: 10274
V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirial analysis,” Machine Learning, vol. 107, no. 8–10, pp. 1537–1560, 2018.
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2018 | Conference (Editor) | LibreCat-ID: 10591
S. Abiteboul et al., Eds., Research Directions for Principles of Data Management, vol. 7, no. 1. 2018, pp. 1–29.
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2018 | Conference Paper | LibreCat-ID: 10181
V.-L. Nguyen, S. Destercke, M.-H. Masson, and E. Hüllermeier, “Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty,” in Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI), 2018, pp. 5089–5095.
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