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Karlson Pfannschmidt
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Extreme F-Measure Maximization using Sparse Probability Estimates
K Jasinska, K Dembczynski, R Busa-Fekete, K Pfannschmidt, T Klerx, ...
International Conference on Machine Learning 33, 1435-1444, 2016
972016
Deep architectures for learning context-dependent ranking functions
K Pfannschmidt, P Gupta, E Hüllermeier
arXiv preprint arXiv:1803.05796, 2018
82018
Evaluating tests in medical diagnosis: combining machine learning with game-theoretical concepts
K Pfannschmidt, E Hüllermeier, S Held, R Neiger
International Conference on Information Processing and Management of …, 2016
72016
Learning context-dependent choice functions
K Pfannschmidt, P Gupta, B Haddenhorst, E Hüllermeier
International Journal of Approximate Reasoning 140, 116-155, 2022
6*2022
Efficient time stepping for numerical integration using reinforcement learning
M Dellnitz, E Hüllermeier, M Lücke, S Ober-Blöbaum, C Offen, S Peitz, ...
arXiv preprint arXiv:2104.03562, 2021
22021
Learning Choice Functions via Pareto-Embeddings
K Pfannschmidt, E Hüllermeier
German Conference on Artificial Intelligence (Künstliche Intelligenz), 327-333, 2020
12020
jPL: A java-based software framework for preference learning
P Gupta, A Hetzer, T Tornede, S Gottschalk, A Kornelsen, S Osterbrink, ...
Proceedings of the LWDA 2017 Workshops: KDML, FGWM, IR, and FGDB, 2017
12017
scikit-optimize/scikit-optimize: High five - v0.5
T Head, MechCoder, G Louppe, I Shcherbatyi, fcharras, Z Vinícius, ...
https://doi.org/10.5281/zenodo.1165540, 2018
2018
Shapley Curves: A New Concept for Modelling Feature Importance
F Adnan, K Pfannschmidt, E Hüllermeier
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Artikelen 1–9