Ewerth, R., Balz, A., Gehlhaar, J., Dembczynski, K., &; Hüllermeier, E. (2015). Depth estimation in monocular images: Quantitative versus qualitative approaches. In F. Hoffmann &; E. Hüllermeier (Eds.), In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany (pp. 235–240). KIT Scientific Publishing.
LibreCat: /record/15750

Lu, S., &; Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In F. Hoffmann &; E. Hüllermeier (Eds.), in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany (pp. 97–104). KIT Scientific Publishing.
LibreCat: /record/15751

Senge, R., &; Hüllermeier, E. (2015). Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
LibreCat: /record/16049

Hüllermeier, E. (2015). From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum, 38(6), 500–509.
LibreCat: /record/16051

Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
LibreCat: /record/16053

Waegeman, W., Dembczynski, K., Jachnik, A., Cheng, W., &; Hüllermeier, E. (2015). On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research, 15, 3313–3368.
LibreCat: /record/16058

Shaker, A., &; Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
LibreCat: /record/16067

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: /record/10234

Hoffmann, F., &; Hüllermeier, E. (2015). Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing.
LibreCat: /record/10235

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).
LibreCat: /record/10236

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).
LibreCat: /record/10237

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: /record/10238

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: /record/10239

Henzgen, S., &; Hüllermeier, E. (2015). Weighted Rank Correlationf: 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: /record/10240

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: /record/10241

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).
LibreCat: /record/10242

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).
LibreCat: /record/10243

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).
LibreCat: /record/10244

Lu, S., &; Hüllermeier, E. (2015). Locally weighted regression through data imprecisiation. In Proceedings 25. Workshop Computational Intelligence (pp. 97–104).
LibreCat: /record/10245

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: /record/10246

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.
LibreCat: /record/10319

Hüllermeier, E. (2015). Does machine learning need fuzzy logic? Fuzzy Sets and Systems, 281, 292–299.
LibreCat: /record/10320

Shaker, A., &; Hüllermeier, E. (2015). Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing, 150, 250–264.
LibreCat: /record/10321

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

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: /record/10323

Senge, R., &; Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033.
LibreCat: /record/10324

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., &; Ramaswamy, A. (2014). Rainbow connection number of graph power and graph products. Graphs and Combinatorics, 30(6), 1363–1382.
LibreCat: /record/24155

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., &; Ramaswamy, A. (2014). Rainbow connection number and radius. Graphs and Combinatorics, 30(2), 275–285.
LibreCat: /record/24156

Mohr, F., &; Walther, S. (2014). Template-based Generation of Semantic Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 188–203).https://doi.org/10.1007/978-3-319-14130-5_14 
LibreCat: /record/353
DOI: 10.1007/978-3-319-14130-5_14

Jungmann, A., Mohr, F., &; Kleinjohann, B. (2014). Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. In Proceedings of the 10th World Congress on Services (SERVICES) (pp. 346–353).https://doi.org/10.1109/SERVICES.2014.68 
LibreCat: /record/447
DOI: 10.1109/SERVICES.2014.68

Jungmann, A., Mohr, F., &; Kleinjohann, B. (2014). Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. In Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA) (pp. 105–112).https://doi.org/10.1109/SOCA.2014.48 
LibreCat: /record/457
DOI: 10.1109/SOCA.2014.48

Mohr, F. (2014). Estimating Functional Reusability of Services. In Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC) (pp. 411–418).
LibreCat: /record/428

Agarwal, M., Fallah Tehrani, A., &; Hüllermeier, E. (2014). Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis, 22(3–4).
LibreCat: /record/16046

Krotzky, T., Fober, T., Hüllermeier, E., &; Klebe, G. (2014). Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 11(5), 878–890.
LibreCat: /record/16060

Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning, 55(7), 1519–1534.
LibreCat: /record/16064

Henzgen, S., Strickert, M., &; Hüllermeier, E. (2014). Visualization of evolving fuzzy-rule-based systems. Evolving Systems, 5, 175–191.
LibreCat: /record/16069

Busa-Fekete, R., Szörenyi, B., Weng, P., Cheng, W., &; Hüllermeier, E. (2014). Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
LibreCat: /record/16077

Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V.,  Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
LibreCat: /record/16078

Strickert, M., Bunte, K., Schleif, F. M., &; Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
LibreCat: /record/16079

Shaker, A., &; Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science, 24(1), 199–212.
LibreCat: /record/16080

Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., &; Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
LibreCat: /record/16082

Donner-Banzhoff, N., Haasenritter, J., Hüllermeier, E., Viniol, A., Bösner, S., &; Becker, A. (2014). The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2(67), 124–132.
LibreCat: /record/16083

Busa-Fekete, R., Szörényi, B., &; Hüllermeier, E. (2014). PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In Proceedings AAAI 2014, Quebec, Canada (pp. 1701–1707).
LibreCat: /record/10247

Busa-Fekete, R., &; Hüllermeier, E. (2014). A Survey of Preference-Based Online Learning with Bandit Algorithms. In Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia (pp. 18–39).
LibreCat: /record/10248

Henzgen, S., &; Hüllermeier, E. (2014). Mining Rank Data. In Proceedings Discovery Science, Bled,Slovenia  (pp. 123–134).
LibreCat: /record/10249

Fallah Tehrani, A., Strickert, M., &; Hüllermeier, E. (2014). The Choquet kernel for monotone data. In Proceedings ESANN , Bruges, Belgium.
LibreCat: /record/10250

Abdel-Aziz, A., Strickert, M., &; Hüllermeier, E. (2014). Learning Solution Similarity in Preference-Based CBR. In Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland (pp. 17–31).
LibreCat: /record/10251

Schäfer, D., &; Hüllermeier, E. (2014). Dyad Ranking Using A Bilinear Plackett-Luce Model. In Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany (pp. 32–33).
LibreCat: /record/10253

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: /record/10254

Fürnkranz, J., Hüllermeier, E., Rudin, C., Slowinski, R., &; Sanner, S. (2014). Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports (Vol. 4, pp. 1–27).
LibreCat: /record/10295