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449 Publications


2017 | Conference Paper | LibreCat-ID: 15397
V. Melnikov and E. Hüllermeier, “Optimizing the structure of nested dichotomies. A comparison of two heuristics,” in in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, 2017, pp. 1–12.
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2017 | Conference Paper | LibreCat-ID: 15399
M. Czech, E. Hüllermeier, M. C. Jacobs, and H. Wehrheim, “Predicting rankings of software verification tools,” in in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017.
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2017 | Conference Paper | LibreCat-ID: 15110
I. Couso, D. Dubois, and E. Hüllermeier, “Maximum likelihood estimation and coarse data,” in in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, 2017, pp. 3–16.
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2017 | Conference Paper | LibreCat-ID: 10204
R. Ewerth et al., “Estimating relative depth in single images via rankboost,” in Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924.
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2017 | Conference Paper | LibreCat-ID: 10205
M. Ahmadi Fahandar, E. Hüllermeier, and I. Couso, “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening,” in Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.
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2017 | Conference Paper | LibreCat-ID: 10206 | OA
F. Mohr, T. Lettmann, and E. Hüllermeier, “Planning with Independent Task Networks,” in Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206.
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2017 | Conference Paper | LibreCat-ID: 10207
M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting rankings of software verification tools,” in Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.
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2017 | Conference Paper | LibreCat-ID: 10208
I. Couso, D. Dubois, and E. Hüllermeier, “Maximum Likelihood Estimation and Coarse Data,” in Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16.
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2017 | Conference Paper | LibreCat-ID: 10209
M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank based on Analogical Reasoning,” in Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017.
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2017 | Conference Paper | LibreCat-ID: 10212
F. Hoffmann, E. Hüllermeier, and R. Mikut, “(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017,” 2017.
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2017 | Conference Paper | LibreCat-ID: 10213
V. Melnikov and E. Hüllermeier, “Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics,” in Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017, 2017, pp. 1–12.
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2017 | Conference Paper | LibreCat-ID: 10216
A. Shaker, W. Heldt, and E. Hüllermeier, “Learning TSK Fuzzy Rules from Data Streams,” in Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia, 2017.
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2017 | Journal Article | LibreCat-ID: 10267
M. Bräuning, E. Hüllermeier, T. Keller, and M. Glaum, “Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application  in accounting,” European Journal of Operational Research, vol. 258, no. 1, pp. 295–306, 2017.
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2017 | Journal Article | LibreCat-ID: 10268
M.-C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, and W. Schäfer, “Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic,” IEEE Transactions on Software Engineering, vol. 43, no. 8, pp. 739–759, 2017.
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2017 | Journal Article | LibreCat-ID: 10269
E. Hüllermeier, “From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection,” The Computing Research Repository  (CoRR), 2017.
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2016 | Journal Article | LibreCat-ID: 24154
A. Ramaswamy and S. Bhatnagar, “Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem,” Stochastics, vol. 88, no. 8, pp. 1173–1187, 2016.
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2016 | Journal Article | LibreCat-ID: 3318
V. Melnikov, E. Hüllermeier, D. Kaimann, B. Frick, and Pritha Gupta, “Pairwise versus Pointwise Ranking: A Case Study,” Schedae Informaticae, vol. 25, 2016.
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2016 | Journal Article | LibreCat-ID: 190
M. C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, and W. Schäfer, “Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic,” IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017, no. 8, pp. 739–759, 2016.
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2016 | Conference Paper | LibreCat-ID: 184
V. Melnikov and E. Hüllermeier, “Learning to Aggregate Using Uninorms,” in Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.
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2016 | Encyclopedia Article | LibreCat-ID: 10785
J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, C. Sammut and G. I. Webb, Eds. Springer, 2016.
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2016 | Conference Paper | LibreCat-ID: 15400
C. Labreuche, E. Hüllermeier, P. Vojtas, and A. Fallah Tehrani, “On the identifiability of models  in multi-criteria preference learning,” in in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, 2016.
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2016 | Conference Paper | LibreCat-ID: 15401
D. Schäfer and E. Hüllermeier, “Preference -based reinforcement learning using dyad ranking,” in in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany, 2016.
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2016 | Conference Paper | LibreCat-ID: 15402
I. Couso, M. Ahmadi Fahandar, and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany, 2016.
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2016 | Conference Paper | LibreCat-ID: 15403
S. Lu and E. Hüllermeier, “Support vector classification on noisy data using fuzzy superset losses,” in in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany, 2016, pp. 1–8.
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2016 | Conference Paper | LibreCat-ID: 15404
D. Schäfer and E. Hüllermeier, “Plackett-Luce networks for dyad ranking,” in in Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany, 2016.
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2016 | Conference Paper | LibreCat-ID: 15111
K. Pfannschmidt, E. Hüllermeier, S. Held, and R. Neiger, “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, 2016, pp. 450–461.
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2016 | Journal Article | LibreCat-ID: 16041
M. Leinweber et al., “CavSimBase: A database for large scale comparison of protein binding sites,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1423–1434, 2016.
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2016 | Dissertation | LibreCat-ID: 141
F. Mohr, Towards Automated Service Composition Under Quality Constraints. Universität Paderborn, 2016.
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2016 | Book Chapter | LibreCat-ID: 10214
J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, C. Sammut and G. I. Webb, Eds. Springer, 2016.
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2016 | Conference (Editor) | LibreCat-ID: 10221
F. Hoffmann, E. Hüllermeier, and R. Mikut, Eds., Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany. 2016.
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2016 | Conference Paper | LibreCat-ID: 10222
K. Jasinska, K. Dembczynski, R. Busa-Fekete, T. Klerx, and E. Hüllermeier, “Extreme F-measure maximization using sparse probability estimates ,” in Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, 2016.
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2016 | Conference Paper | LibreCat-ID: 10223
V. Melnikov and E. Hüllermeier, “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, 2016, pp. 756–771.
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2016 | Conference Paper | LibreCat-ID: 10224
K. Dembczynski, W. Kotlowski, W. Waegeman, R. Busa-Fekete, and E. Hüllermeier, “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, 2016, pp. 511–526.
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2016 | Conference Paper | LibreCat-ID: 10225
A. Shabani, A. Paul, R. Platon, and E. Hüllermeier, “Predicting the electricity consumption of buildings: An improved CBR approach,” in In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 2016, pp. 356–369.
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2016 | Conference Paper | LibreCat-ID: 10226
K. Pfannschmidt, E. Hüllermeier, S. Held, and R. Neiger, “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, 2016, pp. 450–461.
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2016 | Conference Paper | LibreCat-ID: 10227
C. Labreuche, E. Hüllermeier, P. Vojtas, and A. Fallah Tehrani, “On the Identifiability of models in multi-criteria preference learning ,” in Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.
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2016 | Conference Paper | LibreCat-ID: 10228
D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.
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2016 | Conference Paper | LibreCat-ID: 10229
I. Couso, M. Ahmadi Fahandar, and E. Hüllermeier, “Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators,” in Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.
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2016 | Conference Paper | LibreCat-ID: 10230
S. Lu and E. Hüllermeier, “Support vector classification on noisy data using fuzzy supersets losses,” in Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, 2016, pp. 1–8.
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2016 | Conference Paper | LibreCat-ID: 10231
D. Schäfer and E. Hüllermeier, “Plackett-Luce networks for dyad ranking,” in In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.
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2016 | Conference (Editor) | LibreCat-ID: 10263
G. A. Kaminka et al., Eds., ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence, vol. 285. The Hague, The Netherlands: IOS Press, 2016.
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2016 | Journal Article | LibreCat-ID: 10264
M. Leinweber et al., “CavSimBase: A database for large scale comparison of protein binding sites,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1423–1434, 2016.
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2016 | Journal Article | LibreCat-ID: 10266
M. Riemenschneider, R. Senge, U. Neumann, E. Hüllermeier, and D. Heider, “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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2015 | Conference Paper | LibreCat-ID: 280
S. Arifulina, M. C. Platenius, F. Mohr, G. Engels, and W. Schäfer, “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, 2015, pp. 333--340.
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2015 | Journal Article | LibreCat-ID: 323
A. Jungmann and F. Mohr, “An approach towards adaptive service composition in markets of composed services,” Journal of Internet Services and Applications, no. 1, pp. 1–18, 2015.
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2015 | Conference Paper | LibreCat-ID: 324
F. Mohr, “A Metric for Functional Reusability of Services,” in Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313.
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2015 | Conference Paper | LibreCat-ID: 319
F. Mohr, A. Jungmann, and H. Kleine Büning, “Automated Online Service Composition,” in Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64.
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2015 | Journal Article | LibreCat-ID: 4792
R. Senge and E. Hüllermeier, “Fast Fuzzy Pattern Tree Learning for Classification,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2024–2033, 2015.
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2015 | Conference Paper | LibreCat-ID: 15406
D. Schäfer and E. Hüllermeier, “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 co-located ECML/PKDD, Porto, Portugal, 2015, pp. 110–111.
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2015 | Conference Paper | LibreCat-ID: 15749
A. Paul and E. Hüllermeier, “A cbr approach to the angry birds game,” in In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 2015, pp. 68–77.
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2015 | Conference Paper | LibreCat-ID: 15750
R. Ewerth, A. Balz, J. Gehlhaar, K. Dembczynski, and E. Hüllermeier, “Depth estimation in monocular images: Quantitative versus qualitative approaches,” in In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany, 2015, pp. 235–240.
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2015 | Conference Paper | LibreCat-ID: 15751
S. Lu and E. Hüllermeier, “Locally weighted regression through data imprecisiation,” in in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany, 2015, pp. 97–104.
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2015 | Journal Article | LibreCat-ID: 16049
R. Senge and E. Hüllermeier, “Fast fuzzy pattern tree learning for classification ,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2024–2033, 2015.
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2015 | Journal Article | LibreCat-ID: 16051
E. Hüllermeier, “From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions,” Informatik Spektrum, vol. 38, no. 6, pp. 500–509, 2015.
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2015 | Journal Article | LibreCat-ID: 16053
E. Hüllermeier, “Does machine learning need fuzzy logic?,” Fuzzy Sets and Systems, vol. 281, pp. 292–299, 2015.
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2015 | Journal Article | LibreCat-ID: 16058
W. Waegeman, K. Dembczynski, A. Jachnik, W. Cheng, and E. Hüllermeier, “On the Bayes-optimality of F-measure maximizers,” Journal of Machine Learning Research, vol. 15, pp. 3313–3368, 2015.
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2015 | Journal Article | LibreCat-ID: 16067
A. Shaker and E. Hüllermeier, “Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study,” Neurocomputing, vol. 150, pp. 250–264, 2015.
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2015 | Conference Paper | LibreCat-ID: 10234
E. Hüllermeier and M. Minor, “Case-Based Reasoning Research and Development ,” in in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343, 2015.
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2015 | Conference Paper | LibreCat-ID: 10235
F. Hoffmann and E. Hüllermeier, “Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing,” 2015.
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2015 | Conference Paper | LibreCat-ID: 10236
A. Abdel-Aziz and E. Hüllermeier, “Case Base Maintenance in Preference-Based CBR,” in In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 2015, pp. 1–14.
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2015 | Conference Paper | LibreCat-ID: 10237
B. Szörényi, R. Busa-Fekete, P. Weng, and E. Hüllermeier, “Qualitative Multi-Armed Bandits: A Quantile-Based Approach,” in In Proceedings International Conference on Machine Learning (ICML 2015), 2015, pp. 1660–1668.
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2015 | Conference Paper | LibreCat-ID: 10238
D. Schäfer and E. Hüllermeier, “Dyad Ranking Using A Bilinear Plackett-Luce Model,” in in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 227–242.
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2015 | Conference Paper | LibreCat-ID: 10239
E. Hüllermeier and W. Cheng, “Superset Learning Based on Generalized Loss Minimization ,” in in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 2015, pp. 260–275.
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2015 | Conference Paper | LibreCat-ID: 10240
S. Henzgen and E. Hüllermeier, “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), 2015, pp. 422–437.
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2015 | Conference Paper | LibreCat-ID: 10241
B. Szörényi, R. Busa-Fekete, A. Paul, and E. Hüllermeier, “Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach,” in in Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 604–612.
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2015 | Conference Paper | LibreCat-ID: 10242
B. Szörényi, R. Busa-Fekete, K. Dembczynski, and E. Hüllermeier, “Online F-Measure Optimization,” in in Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 595–603.
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2015 | Conference Paper | LibreCat-ID: 10243
A. El Mesaoudi-Paul and E. Hüllermeier, “A CBR Approach to the Angry Birds Game,” in in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015), 2015, pp. 68–77.
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2015 | Conference Paper | LibreCat-ID: 10244
D. Schäfer and E. Hüllermeier, “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), 2015, pp. 110–111.
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2015 | Conference Paper | LibreCat-ID: 10245
S. Lu and E. Hüllermeier, “Locally weighted regression through data imprecisiation,” in Proceedings 25. Workshop Computational Intelligence, 2015, pp. 97–104.
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2015 | Conference Paper | LibreCat-ID: 10246
R. Ewerth, A. Balz, J. Gehlhaar, K. Dembczynski, and E. Hüllermeier, “Depth estimation in monocular images: Quantitative versus qualitative approaches,” in Proceedings 25. Workshop Computational Intelligence, 2015, pp. 235–240.
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2015 | Journal Article | LibreCat-ID: 10319
W. Waegeman, K. Dembczynski, A. Jachnik, W. Cheng, and E. Hüllermeier, “On the Bayes-Optimality of F-Measure Maximizers,” in Journal of Machine Learning Research, vol. 15, pp. 3333–3388, 2015.
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2015 | Journal Article | LibreCat-ID: 10320
E. Hüllermeier, “Does machine learning need fuzzy logic?,” Fuzzy Sets and Systems, vol. 281, pp. 292–299, 2015.
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2015 | Journal Article | LibreCat-ID: 10321
A. Shaker and E. Hüllermeier, “Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study,” Neurocomputing, vol. 150, pp. 250–264, 2015.
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2015 | Journal Article | LibreCat-ID: 10322
E. Hüllermeier, “From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions,” Informatik Spektrum, vol. 38, no. 6, pp. 500–509, 2015.
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2015 | Journal Article | LibreCat-ID: 10323
S. Garcia-Jimenez, U. Bustince, E. Hüllermeier, R. Mesiar, N. R. Pal, and A. Pradera, “Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 4, pp. 1259–1273, 2015.
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2015 | Journal Article | LibreCat-ID: 10324
R. Senge and E. Hüllermeier, “Fast Fuzzy Pattern Tree Learning of Classification,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2024–2033, 2015.
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2014 | Journal Article | LibreCat-ID: 24155
M. Basavaraju, L. S. Chandran, D. Rajendraprasad, and A. Ramaswamy, “Rainbow connection number of graph power and graph products,” Graphs and Combinatorics, vol. 30, no. 6, pp. 1363–1382, 2014.
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2014 | Journal Article | LibreCat-ID: 24156
M. Basavaraju, L. S. Chandran, D. Rajendraprasad, and A. Ramaswamy, “Rainbow connection number and radius,” Graphs and Combinatorics, vol. 30, no. 2, pp. 275–285, 2014.
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2014 | Conference Paper | LibreCat-ID: 353
F. Mohr and S. Walther, “Template-based Generation of Semantic Services,” in Proceedings of the 14th International Conference on Software Reuse (ICSR), 2014, pp. 188–203.
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2014 | Conference Paper | LibreCat-ID: 447
A. Jungmann, F. Mohr, and B. Kleinjohann, “Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services,” in Proceedings of the 10th World Congress on Services (SERVICES), 2014, pp. 346–353.
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2014 | Conference Paper | LibreCat-ID: 457
A. Jungmann, F. Mohr, and B. Kleinjohann, “Applying Reinforcement Learning for Resolving Ambiguity in Service Composition,” in Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA), 2014, pp. 105–112.
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2014 | Conference Paper | LibreCat-ID: 428
F. Mohr, “Estimating Functional Reusability of Services,” in Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC), 2014, pp. 411–418.
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2014 | Journal Article | LibreCat-ID: 16046
M. Agarwal, A. Fallah Tehrani, and E. Hüllermeier, “Preference-based learning of ideal solutions in TOPSIS-like decision models,” Journal of Multi-Criteria Decision Analysis, vol. 22, no. 3–4, 2014.
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2014 | Journal Article | LibreCat-ID: 16060
T. Krotzky, T. Fober, E. Hüllermeier, and G. Klebe, “Extended graph-based models for enhanced similarity search in Cabase,” IEEE/ACM Transactions of Computational Biology and Bioinformatics, vol. 11, no. 5, pp. 878–890, 2014.
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2014 | Journal Article | LibreCat-ID: 16064
E. Hüllermeier, “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization,” International Journal of Approximate Reasoning, vol. 55, no. 7, pp. 1519–1534, 2014.
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2014 | Journal Article | LibreCat-ID: 16069
S. Henzgen, M. Strickert, and E. Hüllermeier, “Visualization of evolving fuzzy-rule-based systems,” Evolving Systems, vol. 5, pp. 175–191, 2014.
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2014 | Journal Article | LibreCat-ID: 16077
R. Busa-Fekete, B. Szörenyi, P. Weng, W. Cheng, and E. Hüllermeier, “Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm.,” Machine Learning, vol. 97, no. 3, pp. 327–351, 2014.
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2014 | Journal Article | LibreCat-ID: 16078
G. Krempl et al., “Open challenges for data stream mining research,” SIGKDD Explorations, vol. 16, no. 1, pp. 1–10, 2014.
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2014 | Journal Article | LibreCat-ID: 16079
M. Strickert, K. Bunte, F. M. Schleif, and E. Hüllermeier, “Correlation-based embedding of pairwise score data,” Neurocomputing, vol. 141, pp. 97–109, 2014.
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2014 | Journal Article | LibreCat-ID: 16080
A. Shaker and E. Hüllermeier, “Survival analysis on data streams: Analyzing temporal events in dynamically changing environments,” International Journal of Applied Mathematics and Computer Science, vol. 24, no. 1, pp. 199–212, 2014.
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2014 | Journal Article | LibreCat-ID: 16082
R. Senge et al., “Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty,” Information Sciences, vol. 255, pp. 16–29, 2014.
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2014 | Journal Article | LibreCat-ID: 16083
N. Donner-Banzhoff, J. Haasenritter, E. Hüllermeier, A. Viniol, S. Bösner, and A. Becker, “The comprehensive diagnostic study is suggested as a design to model the diagnostic process,” Journal of Clinical Epidemiology, vol. 2, no. 67, pp. 124–132, 2014.
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2014 | Conference Paper | LibreCat-ID: 10247
R. Busa-Fekete, B. Szörényi, and E. Hüllermeier, “PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences,” in Proceedings AAAI 2014, Quebec, Canada, 2014, pp. 1701–1707.
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2014 | Conference Paper | LibreCat-ID: 10248
R. Busa-Fekete and E. Hüllermeier, “A Survey of Preference-Based Online Learning with Bandit Algorithms,” in Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia, 2014, pp. 18–39.
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2014 | Conference Paper | LibreCat-ID: 10249
S. Henzgen and E. Hüllermeier, “Mining Rank Data,” in Proceedings Discovery Science, Bled,Slovenia , 2014, pp. 123–134.
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2014 | Conference Paper | LibreCat-ID: 10250
A. Fallah Tehrani, M. Strickert, and E. Hüllermeier, “The Choquet kernel for monotone data,” in Proceedings ESANN , Bruges, Belgium, 2014.
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2014 | Conference Paper | LibreCat-ID: 10251
A. Abdel-Aziz, M. Strickert, and E. Hüllermeier, “Learning Solution Similarity in Preference-Based CBR,” in Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland, 2014, pp. 17–31.
LibreCat
 

2014 | Conference Paper | LibreCat-ID: 10253
D. Schäfer and E. Hüllermeier, “Dyad Ranking Using A Bilinear Plackett-Luce Model,” in Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany, 2014, pp. 32–33.
LibreCat
 

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

2014 | Conference Paper | LibreCat-ID: 10295
J. Fürnkranz, E. Hüllermeier, C. Rudin, R. Slowinski, and S. Sanner, “Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports,” 2014, vol. 4, no. 3, pp. 1–27.
LibreCat
 

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