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146 Publications


2019 | Conference Paper | LibreCat-ID: 10232
Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
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2019 | Conference Abstract | LibreCat-ID: 8956
Hetzer, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. Presented at the European Conference on Data Analysis (ECDA), Bayreuth, Germany.
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2019 | Conference Abstract | LibreCat-ID: 8868
Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany.
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2019 | Conference Abstract | LibreCat-ID: 13132
Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.
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2018 | Conference Paper | LibreCat-ID: 10184
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Proc. 21st Int. Conference on Discovery Science (DS) (pp. 161–175).
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2018 | Conference Paper | LibreCat-ID: 3852
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
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2018 | Conference Paper | LibreCat-ID: 2479
Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039
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2018 | Conference Paper | LibreCat-ID: 10153
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Reduction Stumps for Multi-class Classification. In Proc. 17th Int. Symposium on Intelligent Data Analysis (IDA) (pp. 225–237).
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2018 | Bachelorsthesis | LibreCat-ID: 5936
Scheibl, M. (2018). Learning about learning curves from dataset properties.
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2018 | Conference Paper | LibreCat-ID: 10185
Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., & Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft.
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2018 | Conference Paper | LibreCat-ID: 10154
Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In Proc. 15th Int. Conference on Services Computing (SCC) (pp. 241–244).
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2018 | Conference Paper | LibreCat-ID: 10192
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In Int. Workshop on Automatic Machine Learning (AutoML) at ICML 2018.
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2018 | Journal Article | LibreCat-ID: 10274
Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirial analysis. Machine Learning, 107(8–10), 1537–1560.
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2018 | Conference (Editor) | LibreCat-ID: 10591
Abiteboul, S., Arenas, M., Barceló, P., Bienvenu, M., Calvanese, D., David, C., … Yi, K. (Eds.). (2018). Research Directions for Principles of Data Management (Vol. 7, pp. 1–29).
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2018 | Conference Paper | LibreCat-ID: 10181
Nguyen, V.-L., Destercke, S., Masson, M.-H., & Hüllermeier, E. (2018). Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. In Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI) (pp. 5089–5095).
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2018 | Conference Paper | LibreCat-ID: 2109
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM. https://doi.org/10.1145/3205455.3205562
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2018 | Conference Paper | LibreCat-ID: 2471
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036
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2018 | Book Chapter | LibreCat-ID: 6423
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11
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2018 | Conference Paper | LibreCat-ID: 10148
El Mesaoudi-Paul, A., Hüllermeier, E., & Busa-Fekete, R. (2018). Ranking Distributions based on  Noisy Sorting. In Proc. 35th Int. Conference on Machine Learning (ICML) (pp. 3469–3477).
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2018 | Conference Paper | LibreCat-ID: 3552
Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
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2018 | Conference Paper | LibreCat-ID: 10149
Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. In Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24 (pp. 15–20).
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2018 | Journal Article | LibreCat-ID: 10276
Schäfer, D., & Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Book Chapter | LibreCat-ID: 10783
Couso, I., & Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.
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2018 | Conference Paper | LibreCat-ID: 2857
Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 28th International Conference on Automated Planning and Scheduling. Delft, Netherlands: AAAI.
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2018 | Journal Article | LibreCat-ID: 3402
Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1
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2018 | Bachelorsthesis | LibreCat-ID: 5693
Graf, H. (2018). Ranking of Classification Algorithms in AutoML.
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2018 | Conference Abstract | LibreCat-ID: 1379
Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., & Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart & D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. Stuttgart, Germany.
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2018 | Conference Paper | LibreCat-ID: 10152
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-the-Fly Service Construction with Prototypes. In Proc. 15th Int. Conference on Services Computing (SCC) (pp. 225–232).
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2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar, M., & Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).
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2018 | Journal Article | LibreCat-ID: 3510
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. https://doi.org/10.1007/s10994-018-5735-z
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2018 | Journal Article | LibreCat-ID: 10784
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated machine learning via hierarchical planning. Machine Learning, 107(8–10), 1495–1515.
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2018 | Conference Paper | LibreCat-ID: 10188
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of evolved nested dichotomies for classificaton. In Proc. Genetic and Evolutionary Computation Conference (GECCO) (pp. 561–568).
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2017 | Bachelorsthesis | LibreCat-ID: 5694
Schnitker, N. N. (2017). Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies.
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2017 | Conference Paper | LibreCat-ID: 1180
Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.
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2017 | Conference Paper | LibreCat-ID: 10204
Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T., … Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).
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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 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.
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2017 | Conference Abstract | LibreCat-ID: 5722
Gupta, P., Hetzer, A., Tornede, T., Gottschalk, S., Kornelsen, A., Osterbrink, S., … Hüllermeier, E. (2017). jPL: A Java-based Software Framework for Preference Learning. Presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock.
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2017 | Conference Paper | LibreCat-ID: 10111
Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proceedings of the 40th Annual German Conference on AI (KI 2017) (Vol. 10505, pp. 193–206). Dortmund, Germany: Springer.
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2017 | Conference Paper | LibreCat-ID: 10205
Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).
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2017 | Conference Paper | LibreCat-ID: 10212
Hoffmann, F., Hüllermeier, E., & Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017.
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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 | Encyclopedia Article | LibreCat-ID: 10589
Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).
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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: 10206
Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206).
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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 | Mastersthesis | LibreCat-ID: 5724
Hetzer, A., & Tornede, T. (2017). Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Paderborn.
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2017 | Conference Paper | LibreCat-ID: 71
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262
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2017 | Conference Paper | LibreCat-ID: 115
Jakobs, M.-C., Krämer, J., van Straaten, D., & Lettmann, T. (2017). Certification Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 7–12).
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2017 | Conference Paper | LibreCat-ID: 10214
Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierarchical Planning Versus Evolutionary Optimization . In Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017 (pp. 149–166).
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2017 | Conference Paper | LibreCat-ID: 10207
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26).
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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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2017 | Conference Paper | LibreCat-ID: 3325
Melnikov, V., & Hüllermeier, E. (2017). Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing. https://doi.org/10.5445/KSP/1000074341
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2017 | Conference Paper | LibreCat-ID: 1158
Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., & Hüllermeier, E. (2017). Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 40–45). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W17-2206
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2017 | Report | LibreCat-ID: 72
Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.
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2017 | Conference Paper | LibreCat-ID: 10208
Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16).
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2016 | Conference Paper | LibreCat-ID: 10228
Schäfer, D., & Hüllermeier, E. (2016). Preference-Based Reinforcement Learning Using Dyad Ranking. In R. Busa-Fekete, E. Hüllermeier, V. Mousseau, & K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10223
Melnikov, V., & Hüllermeier, E. (2016). Learning to aggregate using uninorms,  in Proceedings ECML/PKDD-2016. In European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 756–771).
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2016 | Conference Paper | LibreCat-ID: 10230
Lu, S., & Hüllermeier, E. (2016). Support vector classification on noisy data using fuzzy supersets losses. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing (pp. 1–8).
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2016 | Journal Article | LibreCat-ID: 10266
Riemenschneider, M., Senge, R., Neumann, U., Hüllermeier, E., & Heider, D. (2016). Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining, 9(10).
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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: 10224
Dembczynski, K., Kotlowski, W., Waegeman, W., Busa-Fekete, R., & Hüllermeier, E. (2016). Consistency of probalistic classifier trees. In In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy (pp. 511–526).
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2016 | Conference Paper | LibreCat-ID: 10229
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.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10231
Schäfer, D., & Hüllermeier, E. (2016). Plackett-Luce networks for dyad ranking. In In Workshop LWDA “Lernen, Wissen, Daten, Analysen.”
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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: 10225
Shabani, A., Paul, A., Platon, R., & Hüllermeier, E. (2016). Predicting the electricity consumption of buildings: An improved CBR approach. In In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA (pp. 356–369).
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2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka, G. A., Fox, M., Bouquet, P., Hüllermeier, E., Dignum, V., Dignum, F., & van Harmelen, F. (Eds.). (2016). ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence (Vol. 285). The Hague, The Netherlands: IOS Press.
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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 | 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 | Conference Paper | LibreCat-ID: 10226
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 | 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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2016 | Journal Article | LibreCat-ID: 10264
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 | 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 | Conference Paper | LibreCat-ID: 10227
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.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning.
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2016 | Conference Paper | LibreCat-ID: 10222
Jasinska, K., Dembczynski, K., Busa-Fekete, R., Klerx, T., & Hüllermeier, E. (2016). Extreme F-measure maximization using sparse probability estimates . In M. F. Balcan & K. Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA.
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2015 | Conference Paper | LibreCat-ID: 319
Mohr, F., Jungmann, A., & Kleine Büning, H. (2015). Automated Online Service Composition. In Proceedings of the 12th IEEE International Conference on Services Computing (SCC) (pp. 57--64). https://doi.org/10.1109/SCC.2015.18
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2015 | Journal Article | LibreCat-ID: 4792
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033. https://doi.org/10.1109/tfuzz.2015.2396078
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2015 | Conference Paper | LibreCat-ID: 10242
Szörényi, B., Busa-Fekete, R., Dembczynski, K., & Hüllermeier, E. (2015). Online F-Measure Optimization. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 595–603).
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2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann, F., & Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.
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2015 | Journal Article | LibreCat-ID: 10324
Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
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2015 | Conference Paper | LibreCat-ID: 10243
El Mesaoudi-Paul, A., & Hüllermeier, E. (2015). A CBR Approach to the Angry Birds Game. In in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 68–77).
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2015 | Conference Paper | LibreCat-ID: 10236
Abdel-Aziz, A., & Hüllermeier, E. (2015). Case Base Maintenance in Preference-Based CBR. In In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) (pp. 1–14).
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2015 | Journal Article | LibreCat-ID: 10320
Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
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2015 | Journal Article | LibreCat-ID: 323
Jungmann, A., & Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications, (1), 1–18. https://doi.org/10.1186/s13174-015-0022-8
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2015 | Conference Paper | LibreCat-ID: 10244
Schäfer, D., & Hüllermeier, E. (2015). Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations. In in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML) (pp. 110–111).
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2015 | Conference Paper | LibreCat-ID: 10237
Szörényi, B., Busa-Fekete, R., Weng, P., & Hüllermeier, E. (2015). Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In In Proceedings International Conference on Machine Learning (ICML 2015) (pp. 1660–1668).
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2015 | Journal Article | LibreCat-ID: 10319
Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., & Hüllermeier, E. (2015). On the Bayes-Optimality of F-Measure Maximizers. In Journal of Machine Learning Research, 15, 3333–3388.
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2015 | Journal Article | LibreCat-ID: 10321
Shaker, A., & Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
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2015 | Conference Paper | LibreCat-ID: 324
Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313). https://doi.org/10.1007/978-3-319-14130-5_21
LibreCat | Files available | DOI
 

2015 | Conference Paper | LibreCat-ID: 280
Arifulina, S., Platenius, M. C., Mohr, F., Engels, G., & Schäfer, W. (2015). Market-Specific Service Compositions: Specification and Matching. In Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet (pp. 333--340). https://doi.org/10.1109/SERVICES.2015.58
LibreCat | Files available | DOI
 

2015 | Conference Paper | LibreCat-ID: 10240
Henzgen, S., & Hüllermeier, E. (2015). Weighted Rank Correlation : A Flexible Approach Based on Fuzzy Order Relations. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 422–437).
LibreCat
 

2015 | Conference Paper | LibreCat-ID: 10238
Schäfer, D., & Hüllermeier, E. (2015). Dyad Ranking Using A Bilinear Plackett-Luce Model. In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 227–242).
LibreCat
 

2015 | Conference Paper | LibreCat-ID: 10245
Lu, S., & Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In Proceedings 25. Workshop Computational Intelligence (pp. 97–104).
LibreCat
 

2015 | Journal Article | LibreCat-ID: 10322
Hüllermeier, E. (2015). From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
LibreCat
 

2015 | Conference Paper | LibreCat-ID: 10234
Hüllermeier, E., & Minor, M. (2015). Case-Based Reasoning Research and Development . In in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer.
LibreCat
 

2015 | Conference Paper | LibreCat-ID: 10241
Szörényi, B., Busa-Fekete, R., Paul, A., & Hüllermeier, E. (2015). Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In in Advances in Neural Information Processing Systems 28 (NIPS 2015) (pp. 604–612).
LibreCat
 

2015 | Conference Paper | LibreCat-ID: 10246
Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., & Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In Proceedings 25. Workshop Computational Intelligence (pp. 235–240).
LibreCat
 

2015 | Conference Paper | LibreCat-ID: 10239
Hüllermeier, E., & Cheng, W. (2015). Superset Learning Based on Generalized Loss Minimization . In in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (pp. 260–275).
LibreCat
 

2015 | Journal Article | LibreCat-ID: 10323
Garcia-Jimenez, S., Bustince, U., Hüllermeier, E., Mesiar, R., Pal, N. R., & Pradera, A. (2015). Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 23(4), 1259–1273.
LibreCat
 

2014 | Conference Paper | LibreCat-ID: 10254
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In Proceedings, Parts I-III. Lecture Notes in Computer Science (pp. 8724–8726). Springer.
LibreCat
 

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