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449 Publications


2019 | Preprint | LibreCat-ID: 18018
Uniform approximation in classical weak convergence theory
V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).
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2019 | Conference Abstract | LibreCat-ID: 8868
Towards Automated Machine Learning for Multi-Label Classification
M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.
LibreCat | Files available
 

2019 | Journal Article | LibreCat-ID: 10578
Choice Functions Generated by Mallows and Plackett–Luce Relations
V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213.
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2019 | Journal Article | LibreCat-ID: 15001
Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning
I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence Magazine (2019) 31–44.
LibreCat | DOI
 

2019 | Journal Article | LibreCat-ID: 15002 | OA
Multi-target prediction: a unifying view on problems and methods
W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324.
LibreCat | Files available | DOI
 

2019 | Conference Paper | LibreCat-ID: 15003
Set-Valued Prediction in Multi-Class Classification
T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in: Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, 2019.
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2019 | Book Chapter | LibreCat-ID: 15004
Feature Selection for Analogy-Based Learning to Rank
M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.
LibreCat | DOI
 

2019 | Book Chapter | LibreCat-ID: 15005
Analogy-Based Preference Learning with Kernels
M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial Intelligence, Cham, 2019.
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2019 | Book Chapter | LibreCat-ID: 15006
Epistemic Uncertainty Sampling
V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham, 2019.
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2019 | Conference Paper | LibreCat-ID: 15007 | OA
Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA
V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101), 2019.
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