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449 Publications


2017 | Conference Paper | LibreCat-ID: 10213
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 1–12).
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2017 | Conference Paper | LibreCat-ID: 10216
Shaker, A., Heldt, W., & Hüllermeier, E. (2017). Learning TSK Fuzzy Rules from Data Streams. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia.
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2017 | Journal Article | LibreCat-ID: 10267
Bräuning, M., Hüllermeier, E., Keller, T., & Glaum, M. (2017). Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting. European Journal of Operational Research, 258(1), 295–306.
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2017 | Journal Article | LibreCat-ID: 10268
Platenius, M.-C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2017). Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Transactions on Software Engineering, 43(8), 739–759.
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2017 | Journal Article | LibreCat-ID: 10269
Hüllermeier, E. (2017). From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection. The Computing Research Repository  (CoRR).
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2016 | Journal Article | LibreCat-ID: 24154
Ramaswamy, A., & Bhatnagar, S. (2016). Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem. Stochastics, 88(8), 1173–1187.
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2016 | Journal Article | LibreCat-ID: 3318
Melnikov, V., Hüllermeier, E., Kaimann, D., Frick, B., & Gupta, Pritha . (2016). Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae, 25. https://doi.org/10.4467/20838476si.16.006.6187
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2016 | Journal Article | LibreCat-ID: 190
Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W. (2016). Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented at ICSE 2017, (8), 739–759. https://doi.org/10.1109/TSE.2016.2632115
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2016 | Conference Paper | LibreCat-ID: 184
Melnikov, V., & Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771). https://doi.org/10.1007/978-3-319-46227-1_47
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2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference Paper | LibreCat-ID: 15400
Labreuche, C., Hüllermeier, E., Vojtas, P., & Fallah Tehrani, A. (2016). On the identifiability of models  in multi-criteria preference learning. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15401
Schäfer, D., & Hüllermeier, E. (2016). Preference -based reinforcement learning using dyad ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany.
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2016 | Conference Paper | LibreCat-ID: 15402
Couso, I., Ahmadi Fahandar, M., & Hüllermeier, E. (2016). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany.
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2016 | Conference Paper | LibreCat-ID: 15403
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy superset losses. In E. Hüllermeier, F. Hoffmann, & R. Mikut (Eds.), in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany (pp. 1–8). KIT Scientific Publishing.
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2016 | Conference Paper | LibreCat-ID: 15404
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany.
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2016 | Conference Paper | LibreCat-ID: 15111
Pfannschmidt, K., Hüllermeier, E., Held, S., & Neiger, R. (2016). Evaluating tests in medical  diagnosis-Combining machine learning with game-theoretical concepts. In In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands (pp. 450–461). Springer.
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2016 | Journal Article | LibreCat-ID: 16041
Leinweber, M., Fober, T., Strickert, M., Baumgärtner, L., Klebe, G., Freisleben, B., & Hüllermeier, E. (2016). CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1423–1434.
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2016 | Dissertation | LibreCat-ID: 141
Mohr, F. (2016). Towards Automated Service Composition Under Quality Constraints. Universität Paderborn. https://doi.org/10.17619/UNIPB/1-171
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2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz, J., & Hüllermeier, E. (2016). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining. Springer.
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2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann, F., Hüllermeier, E., & Mikut, R. (Eds.). (2016). Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.
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