Eyke Hüllermeier
Eyke Hüllermeier
Professor of Computer Science, Paderborn University
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Zitiert von
Zitiert von
ChatGPT for good? On opportunities and challenges of large language models for education
E Kasneci, K Seßler, S Küchemann, M Bannert, D Dementieva, F Fischer, ...
Learning and individual differences 103, 102274, 2023
Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
E Hüllermeier, W Waegeman
Machine learning 110 (3), 457-506, 2021
Multilabel classification via calibrated label ranking
J Fürnkranz, E Hüllermeier, E Loza Mencía, K Brinker
Machine learning 73, 133-153, 2008
Preference learning and ranking by pairwise comparison
J Fürnkranz, E Hüllermeier
Preference learning, 65-82, 2011
Preference learning
J Fürnkranz, E Hüllermeier
Encyclopedia of Machine Learning, 789-795, 2010
Label ranking by learning pairwise preferences
E Hüllermeier, J Fürnkranz, W Cheng, K Brinker
Artificial Intelligence 172 (16-17), 1897-1916, 2008
Bayes optimal multilabel classification via probabilistic classifier chains
W Cheng, E Hüllermeier, KJ Dembczynski
Proceedings of the 27th international conference on machine learning (ICML …, 2010
FURIA: an algorithm for unordered fuzzy rule induction
J Hühn, E Hüllermeier
Data Mining and Knowledge Discovery 19, 293-319, 2009
Combining instance-based learning and logistic regression for multilabel classification
W Cheng, E Hüllermeier
Machine Learning 76, 211-225, 2009
An approach to modelling and simulation of uncertain dynamical systems
E Hüllermeier
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 1997
On label dependence and loss minimization in multi-label classification
K Dembczyński, W Waegeman, W Cheng, E Hüllermeier
Machine Learning 88, 5-45, 2012
Open challenges for data stream mining research
G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ...
ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014
Online clustering of parallel data streams
J Beringer, E Hüllermeier
Data & knowledge engineering 58 (2), 180-204, 2006
Pairwise preference learning and ranking
J Fürnkranz, E Hüllermeier
European conference on machine learning, 145-156, 2003
Learning from ambiguously labeled examples
E Hüllermeier, J Beringer
Intelligent Data Analysis 10 (5), 419-439, 2006
Grouping, overlap, and generalized bientropic functions for fuzzy modeling of pairwise comparisons
H Bustince, M Pagola, R Mesiar, E Hullermeier, F Herrera
IEEE Transactions on Fuzzy Systems 20 (3), 405-415, 2011
Fuzzy methods in machine learning and data mining: Status and prospects
E Hüllermeier
Fuzzy sets and Systems 156 (3), 387-406, 2005
A systematic approach to the assessment of fuzzy association rules
D Dubois, E Hüllermeier, H Prade
Data Mining and Knowledge Discovery 13, 167-192, 2006
Preferences in AI: An overview
C Domshlak, E Hüllermeier, S Kaci, H Prade
Artificial Intelligence 175 (7-8), 1037-1052, 2011
ML-Plan: Automated machine learning via hierarchical planning
F Mohr, M Wever, E Hüllermeier
Machine Learning 107, 1495-1515, 2018
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