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262 Publications


2018 | Book Chapter | LibreCat-ID: 10783
Couso, Ines, and Eyke Hüllermeier. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” Frontiers in Computational Intelligence, edited by Sanaz Mostaghim et al., Springer, 2018, pp. 31–46.
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2018 | Conference Paper | LibreCat-ID: 2857
Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 28th International Conference on Automated Planning and Scheduling, AAAI, 2018.
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2018 | Journal Article | LibreCat-ID: 3402
Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018, doi:10.1007/s10994-018-5733-1.
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2018 | Bachelorsthesis | LibreCat-ID: 5693
Graf, Helena. Ranking of Classification Algorithms in AutoML. 2018.
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2018 | Conference Abstract | LibreCat-ID: 1379
Seemann, Nina, et al. “Supporting the Cognitive Process in Annotation Tasks.” Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, edited by Kerstin Eckart and Dominik Schlechtweg, 2018.
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2018 | Conference Paper | LibreCat-ID: 10152
Mohr, Felix, et al. “On-the-Fly Service Construction with Prototypes.” Proc. 15th Int. Conference on Services Computing (SCC), 2018, pp. 225–32.
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2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” Proc. 32 Nd AAAI Conference on Artificial Intelligence (AAAI), 2018, pp. 2951–58.
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2018 | Journal Article | LibreCat-ID: 10784
Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, vol. 107, no. 8–10, 2018, pp. 1495–515.
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2018 | Conference Paper | LibreCat-ID: 10188
Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classificaton.” Proc. Genetic and Evolutionary Computation Conference (GECCO), 2018, pp. 561–68.
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2018 | Journal Article | LibreCat-ID: 3510
Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, Springer, 2018, doi:10.1007/s10994-018-5735-z.
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