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5943 Publications
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 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: 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: 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.
LibreCat
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 | 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 | 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.
LibreCat
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: 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).
LibreCat
2016 | Journal Article | LibreCat-ID: 1373
Herlich, M., Bredenbals, N., & Karl, H. (2016). Delayed (de-)activation in servers with a sleep mode. Sustainable Computing: Informatics and Systems, 10, 48–55. https://doi.org/10.1016/j.suscom.2016.04.002
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| DOI
2016 | Conference Paper | LibreCat-ID: 137 | 
Heindorf, S., Potthast, M., Stein, B., & Engels, G. (2016). Vandalism Detection in Wikidata. Proceedings of the 25th International Conference on Information and Knowledge Management (CIKM 2016), 327--336. https://doi.org/10.1145/2983323.2983740
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2016 | Book Chapter | LibreCat-ID: 2978
Blömer, J., & Bujna, K. (2016). Adaptive Seeding for Gaussian Mixture Models. In Advances in Knowledge Discovery and Data Mining (pp. 296–308). Springer International Publishing. https://doi.org/10.1007/978-3-319-31750-2_24
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| DOI
2016 | Journal Article | LibreCat-ID: 48306
Habernal, I., & Gurevych, I. (2016). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics, 43(1), 125–179. https://doi.org/10.1162/coli_a_00276
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| DOI
2016 | Conference Paper | LibreCat-ID: 48308
Habernal, I., Sukhareva, M., Raiber, F., Shtok, A., Kurland, O., Ronen, H., Bar-Ilan, J., & Gurevych, I. (2016). New Collection Announcement. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/2911451.2914682
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| DOI
2016 | Conference Paper | LibreCat-ID: 48307
Habernal, I., & Gurevych, I. (2016). Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p16-1150
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| DOI
2016 | Conference Paper | LibreCat-ID: 46364
Blot, A., Hoos, H., Jourdan, L., Marmion, M., & Trautmann, H. (2016). MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In V. et al. Joaquin (Ed.), LION 2016: Learning and Intelligent Optimization (Vol. 10079, pp. 32–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_3
LibreCat
| DOI
2016 | Book Chapter | LibreCat-ID: 46363
Rudolph, G., Schütze, O., & Trautmann, H. (2016). On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front. In G. Squillero & P. Burelli (Eds.), Applications of Evolutionary Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II (pp. 42–55). Springer International Publishing. https://doi.org/10.1007/978-3-319-31153-1_4
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| DOI
2016 | Conference Paper | LibreCat-ID: 46369
Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes. Proceedings of the 14$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XIV), 962–972. https://doi.org/10.1007/978-3-319-45823-6_90
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| DOI
2016 | Conference Paper | LibreCat-ID: 46367
Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary Computation, 229–236. https://doi.org/10.1145/2908812.2908845
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| DOI
2016 | Journal Article | LibreCat-ID: 46371
Rudolph, G., Schütze, O., Grimme, C., Domínguez-Medina, C., & Trautmann, H. (2016). Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results. Computational Optimization and Applications (Comput. Optim. Appl.), 64(2), 589–618. https://doi.org/10.1007/s10589-015-9815-8
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