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134 Publications

2018 | Journal Article | LibreCat-ID: 10784
Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated machine learning via hierarchical planning. Machine Learning. 2018;107(8-10):1495-1515.

2018 | Conference Paper | LibreCat-ID: 10188
Wever MD, Mohr F, Hüllermeier E. Ensembles of evolved nested dichotomies for classificaton. In: Proc. Genetic and Evolutionary Computation Conference (GECCO). ; 2018:561-568.

2017 | Bachelorsthesis | LibreCat-ID: 5694
Schnitker NN. Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies.; 2017.

2017 | Conference Paper | LibreCat-ID: 1180
Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence. Dortmund; 2017.
LibreCat | Files available | Download (ext.)

2017 | Conference Paper | LibreCat-ID: 10204
Ewerth R, Springstein M, Müller E, et al. Estimating relative depth in single images via rankboost. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017). ; 2017:919-924.

2017 | Conference Paper | LibreCat-ID: 10216
Shaker A, Heldt W, Hüllermeier E. Learning TSK Fuzzy Rules from Data Streams. In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia. ; 2017.

2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar M, Hüllermeier E. Learning to Rank based on Analogical Reasoning. In: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence. ; 2017.

2017 | Conference Abstract | LibreCat-ID: 5722
Gupta P, Hetzer A, Tornede T, et al. jPL: A Java-based Software Framework for Preference Learning. In: ; 2017.

2017 | Conference Paper | LibreCat-ID: 10111
Mohr F, Lettmann T, Hüllermeier E. Planning with Independent Task Networks. In: Proceedings of the 40th Annual German Conference on AI (KI 2017). Vol 10505. Lecture Notes in Computer Science. Springer; 2017:193-206.

2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar M, Hüllermeier E, Couso I. Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In: Proc. 34th Int. Conf. on Machine Learning (ICML 2017). ; 2017:1078-1087.

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