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Schäfer, D., and Eyke Hüllermeier. “Preference -Based Reinforcement Learning Using Dyad Ranking.” In Proceedings DPL16 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany, edited by R. Busa-Fekete et al., 2016.
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Couso, Ines, et al. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” In Proceedings DPL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, edited by R. Busa-Fekete et al., 2016.
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Lu, S., and Eyke Hüllermeier. “Support Vector Classification on Noisy Data Using Fuzzy Superset Losses.” In Proceedings 26th Workshop Computational Intelligence, Dortmund Germany, edited by Eyke Hüllermeier et al., KIT Scientific Publishing, 2016, pp. 1–8.
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Schäfer, D., and Eyke Hüllermeier. “Plackett-Luce Networks for Dyad Ranking.” In Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany, 2016.
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Pfannschmidt, Karlson, et al. “Evaluating Tests in Medical  Diagnosis-Combining Machine Learning with Game-Theoretical Concepts.” In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–61.
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Leinweber, M., et al. “CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites.” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, 2016, pp. 1423–34.
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Mohr, Felix. Towards Automated Service Composition Under Quality Constraints. Universität Paderborn, 2016, doi:10.17619/UNIPB/1-171.
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DOI: 10.17619/UNIPB/1-171

Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, Springer, 2016.
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Hoffmann, F., et al., editors.  Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany. 2016.
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Jasinska, K., et al. “Extreme F-Measure Maximization Using Sparse Probability Estimates .” Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, edited by M.F. Balcan and K.Q. Weinberger, 2016.
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Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms,  in Proceedings ECML/PKDD-2016.” European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 756–71.
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Dembczynski, K., et al. “Consistency of Probalistic Classifier Trees.” In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 511–26.
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Shabani, Aulon, et al. “Predicting the Electricity Consumption of Buildings: An Improved CBR Approach.” In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 2016, pp. 356–69.
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Pfannschmidt, Karlson, et al. “Evaluating Tests in Medical  Diagnosis-Combining Machine Learning with Game-Theoretical Concepts.” In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–61.
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Labreuche, C., et al. “On the Identifiability of Models in Multi-Criteria Preference Learning .” Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete et al., 2016.
LibreCat: /record/10227

Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete et al., 2016.
LibreCat: /record/10228

Couso, Ines, et al. “Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators.” Proceedings DPL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, edited by Robert Busa-Fekete et al., 2016.
LibreCat: /record/10229

Lu, S., and Eyke Hüllermeier. “Support Vector Classification on Noisy Data Using Fuzzy Supersets Losses.” Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, edited by F. Hoffmann et al., 2016, pp. 1–8.
LibreCat: /record/10230

Schäfer, Dirk, and Eyke Hüllermeier. “Plackett-Luce Networks for Dyad Ranking.” In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.
LibreCat: /record/10231