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.
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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.
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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.
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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.
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Schäfer, Dirk, and Eyke Hüllermeier. “Plackett-Luce Networks for Dyad Ranking.” In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.
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Kaminka, G. A., et al., editors. ECAI 2016, 22nd European Conference on Artificial Intelligence, Including PAIS 2016, Prestigious Applications of Artificial Intelligence. Vol. 285, IOS Press, 2016.
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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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Riemenschneider, M., et al. “Exploiting HIV-1 Protease and Reverse Transcriptase Cross-Resistance Information for Improved Drug Resistance Prediction by Means of Multi-Label Classification.” BioData Mining, vol. 9, no. 10, 2016.
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Arifulina, Svetlana, et al. “Market-Specific Service Compositions: Specification and Matching.”10.1109/SERVICES.2015.58__Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340, doi:10.1109/SERVICES.2015.58.
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Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.”10.1186/s13174-015-0022-8__Journal of Internet Services and Applications, no. 1, Springer, 2015, pp. 1–18, doi:10.1186/s13174-015-0022-8.
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Mohr, Felix. “A Metric for Functional Reusability of Services.”10.1007/978-3-319-14130-5_21__Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313, doi:10.1007/978-3-319-14130-5_21.
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Mohr, Felix, et al. “Automated Online Service Composition.”10.1109/SCC.2015.18__Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64, doi:10.1109/SCC.2015.18.
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Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification.”10.1109/tfuzz.2015.2396078__IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, Institute of Electrical and Electronics Engineers (IEEE), 2015, pp. 2024–33, doi:10.1109/tfuzz.2015.2396078.
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Schäfer, D., and Eyke Hüllermeier. “Preference-Based Meta-Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations.” In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection Co-Located ECML/PKDD, Porto, Portugal, 2015, pp. 110–11.
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Paul, Adil, and Eyke Hüllermeier. “A Cbr Approach to the Angry Birds Game.” In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 2015, pp. 68–77.
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Ewerth, R., et al. “Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches.” In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany, edited by F. Hoffmann and Eyke Hüllermeier, KIT Scientific Publishing, 2015, pp. 235–40.
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Lu, S., and Eyke Hüllermeier. “Locally Weighted Regression through Data Imprecisiation.” In Proceedings 25th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann and Eyke Hüllermeier, KIT Scientific Publishing, 2015, pp. 97–104.
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Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification .” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, 2015, pp. 2024–33.
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Hüllermeier, Eyke. “From Knowledge-Based to Data Driven Fuzzy Modeling: Development, Criticism and Alternative Directions.” Informatik Spektrum, vol. 38, no. 6, 2015, pp. 500–09.
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Hüllermeier, Eyke. “Does Machine Learning Need Fuzzy Logic?” Fuzzy Sets and Systems, vol. 281, 2015, pp. 292–99.
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Waegeman, W., et al. “On the Bayes-Optimality of F-Measure Maximizers.” Journal of Machine Learning Research, vol. 15, 2015, pp. 3313–68.
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Shaker, A., and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams: Experimental Design and Case Study.” Neurocomputing, vol. 150, 2015, pp. 250–64.
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Hüllermeier, Eyke, and M. Minor. “Case-Based Reasoning Research and Development .” In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343, Springer, 2015.
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Hoffmann, F., and Eyke Hüllermeier. Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing. 2015.
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Abdel-Aziz, A., and Eyke Hüllermeier. “Case Base Maintenance in Preference-Based CBR.” In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 2015, pp. 1–14.
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Szörényi, B., et al. “Qualitative Multi-Armed Bandits: A Quantile-Based Approach.” In Proceedings International Conference on Machine Learning (ICML 2015), 2015, pp. 1660–68.
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Schäfer, Dirk, and Eyke Hüllermeier. “Dyad Ranking Using A Bilinear Plackett-Luce Model.” In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 227–42.
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Hüllermeier, Eyke, and W. Cheng. “Superset Learning Based on Generalized Loss Minimization .” In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 260–75.
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Henzgen, Sascha, and Eyke Hüllermeier. “Weighted Rank Correlationf: A Flexible Approach Based on Fuzzy Order Relations.” In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 422–37.
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Szörényi, B., et al. “Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach.” In Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 604–12.
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Szörényi, B., et al. “Online F-Measure Optimization.” In Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 595–603.
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El Mesaoudi-Paul, Adil, and Eyke Hüllermeier. “A CBR Approach to the Angry Birds Game.” In Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 2015, pp. 68–77.
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Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations.” In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML), 2015, pp. 110–11.
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Lu, S., and Eyke Hüllermeier. “Locally Weighted Regression through Data Imprecisiation.” Proceedings 25. Workshop Computational Intelligence, 2015, pp. 97–104.
LibreCat: /record/10245

Ewerth, Ralph, et al. “Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches.” Proceedings 25. Workshop Computational Intelligence, 2015, pp. 235–40.
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Waegeman, W., et al. “On the Bayes-Optimality of F-Measure Maximizers.” In Journal of Machine Learning Research, vol. 15, 2015, pp. 3333–88.
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Hüllermeier, Eyke. “Does Machine Learning Need Fuzzy Logic?” Fuzzy Sets and Systems, vol. 281, 2015, pp. 292–99.
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Shaker, Ammar, and Eyke Hüllermeier. “Recovery Analysis for Adaptive Learning from Non-Stationary Data Streams: Experimental Design and Case Study.” Neurocomputing, vol. 150, 2015, pp. 250–64.
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Hüllermeier, Eyke. “From Knowledge-Based to Data-Driven Fuzzy Modeling-Development, Criticism and Alternative Directions.” Informatik Spektrum, vol. 38, no. 6, 2015, pp. 500–09.
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Garcia-Jimenez, S., et al. “Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems.” IEEE Transactions on Fuzzy Systems, vol. 23, no. 4, 2015, pp. 1259–73.
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Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning of Classification.” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, 2015, pp. 2024–33.
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Basavaraju, Manu, et al. “Rainbow Connection Number of Graph Power and Graph Products.” Graphs and Combinatorics, vol. 30, no. 6, Springer, 2014, pp. 1363–82.
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Basavaraju, Manu, et al. “Rainbow Connection Number and Radius.” Graphs and Combinatorics, vol. 30, no. 2, Springer, 2014, pp. 275–85.
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Mohr, Felix, and Sven Walther. “Template-Based Generation of Semantic Services.”10.1007/978-3-319-14130-5_14__Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203, doi:10.1007/978-3-319-14130-5_14.
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DOI: 10.1007/978-3-319-14130-5_14

Jungmann, Alexander, et al. “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services.”10.1109/SERVICES.2014.68__Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–53, doi:10.1109/SERVICES.2014.68.
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DOI: 10.1109/SERVICES.2014.68