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428 Publications


2017 | Conference Paper | LibreCat-ID: 71
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Tools. In: Proceedings of the 3rd International Workshop on Software Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262
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2017 | Book Chapter | LibreCat-ID: 10784
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Vol 107. Springer; 2017:1000-1005.
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2017 | Conference Paper | LibreCat-ID: 10208
Couso I, Dubois D, Hüllermeier E. Maximum Likelihood Estimation and Coarse Data. In: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017). ; 2017:3-16.
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2017 | Conference Paper | LibreCat-ID: 1158
Seemann N, Merten M-L, Geierhos M, Tophinke D, Hüllermeier E. Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL); 2017:40-45. doi:10.18653/v1/W17-2206
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2017 | Conference Paper | LibreCat-ID: 15397
Melnikov V, Hüllermeier E. Optimizing the structure of nested dichotomies. A comparison of two heuristics. In: Hoffmann F, Hüllermeier E, Mikut R, eds. In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2017:1-12.
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2017 | Report | LibreCat-ID: 72
Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Competitions.; 2017.
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2017 | Conference Paper | LibreCat-ID: 3325
Melnikov V, Hüllermeier E. Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. KIT Scientific Publishing; 2017. doi:10.5445/KSP/1000074341
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2016 | Encyclopedia Article | LibreCat-ID: 10785
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.
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2016 | Conference Paper | LibreCat-ID: 10223
Melnikov V, Hüllermeier E. 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. ; 2016:756-771.
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2016 | Conference Paper | LibreCat-ID: 10228
Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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
Lu S, Hüllermeier E. Support vector classification on noisy data using fuzzy supersets losses. In: Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing. ; 2016:1-8.
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2016 | Journal Article | LibreCat-ID: 10266
Riemenschneider M, Senge R, Neumann U, Hüllermeier E, Heider D. Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Mining. 2016;9(10).
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2016 | Conference Paper | LibreCat-ID: 15401
Schäfer D, Hüllermeier E. Preference -based reinforcement learning using dyad ranking. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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: 10224
Dembczynski K, Kotlowski W, Waegeman W, Busa-Fekete R, Hüllermeier E. 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. ; 2016:511-526.
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2016 | Conference Paper | LibreCat-ID: 10229
Couso I, Ahmadi Fahandar M, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
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2016 | Conference Paper | LibreCat-ID: 10231
Schäfer D, Hüllermeier E. Plackett-Luce networks for dyad ranking. In: In Workshop LWDA “Lernen, Wissen, Daten, Analysen.” ; 2016.
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2016 | Conference Paper | LibreCat-ID: 15402
Couso I, Ahmadi Fahandar M, Hüllermeier E. Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. 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: 15111
Pfannschmidt K, Hüllermeier E, Held S, Neiger R. 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. Springer; 2016:450-461.
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2016 | Conference Paper | LibreCat-ID: 10225
Shabani A, Paul A, Platon R, Hüllermeier E. Predicting the electricity consumption of buildings: An improved CBR approach. In: In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA. ; 2016:356-369.
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2016 | Conference (Editor) | LibreCat-ID: 10263
Kaminka GA, Fox M, Bouquet P, 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 | Conference Paper | LibreCat-ID: 15403
Lu S, Hüllermeier E. Support vector classification on noisy data using fuzzy superset losses. In: Hüllermeier E, Hoffmann F, Mikut R, eds. In Proceedings 26th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2016:1-8.
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2016 | Journal Article | LibreCat-ID: 190
Platenius MC, Shaker A, Becker M, Hüllermeier E, Schäfer W. Imprecise Matching of Requirements Specifications for Software Services using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017. 2016;(8):739-759. doi:10.1109/TSE.2016.2632115
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2016 | Book Chapter | LibreCat-ID: 10214
Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Springer; 2016.
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2016 | Conference (Editor) | LibreCat-ID: 10221
Hoffmann F, Hüllermeier E, Mikut R, eds. Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany.; 2016.
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2016 | Conference Paper | LibreCat-ID: 10226
Pfannschmidt K, Hüllermeier E, Held S, Neiger R. 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. Springer; 2016:450-461.
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2016 | Journal Article | LibreCat-ID: 10264
Leinweber M, Fober T, Strickert M, et al. CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering. 2016;28(6):1423-1434.
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2016 | Dissertation | LibreCat-ID: 141
Mohr F. Towards Automated Service Composition Under Quality Constraints. Universität Paderborn; 2016. doi:10.17619/UNIPB/1-171
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2016 | Conference Paper | LibreCat-ID: 15404
Schäfer D, Hüllermeier E. Plackett-Luce networks for dyad ranking. In: In Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany. ; 2016.
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2016 | Journal Article | LibreCat-ID: 16041
Leinweber M, Fober T, Strickert M, et al. CavSimBase: A database for large scale comparison of protein binding sites. IEEE Transactions on Knowledge and Data Engineering. 2016;28(6):1423-1434.
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2016 | Conference Paper | LibreCat-ID: 184
Melnikov V, Hüllermeier E. Learning to Aggregate Using Uninorms. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016). LNCS. ; 2016:756-771. doi:10.1007/978-3-319-46227-1_47
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2016 | Conference Paper | LibreCat-ID: 10222
Jasinska K, Dembczynski K, Busa-Fekete R, Klerx T, Hüllermeier E. Extreme F-measure maximization using sparse probability estimates . In: Balcan MF, Weinberger KQ, eds. Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA. ; 2016.
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2016 | Conference Paper | LibreCat-ID: 10227
Labreuche C, Hüllermeier E, Vojtas P, Fallah Tehrani A. On the Identifiability of models in multi-criteria preference learning . In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning. ; 2016.
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2016 | Conference Paper | LibreCat-ID: 15400
Labreuche C, Hüllermeier E, Vojtas P, Fallah Tehrani A. On the identifiability of models  in multi-criteria preference learning. In: Busa-Fekete R, Hüllermeier E, Mousseau V, Pfannschmidt K, eds. In Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany. ; 2016.
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2016 | Journal Article | LibreCat-ID: 3318
Melnikov V, Hüllermeier E, Kaimann D, Frick B, Gupta Pritha . Pairwise versus Pointwise Ranking: A Case Study. Schedae Informaticae. 2016;25. doi:10.4467/20838476si.16.006.6187
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2015 | Journal Article | LibreCat-ID: 10324
Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning of Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.
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2015 | Conference Paper | LibreCat-ID: 10235
Hoffmann F, Hüllermeier E. Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing. In: ; 2015.
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2015 | Conference Paper | LibreCat-ID: 10242
Szörényi B, Busa-Fekete R, Dembczynski K, Hüllermeier E. Online F-Measure Optimization. In: In Advances in Neural Information Processing Systems 28 (NIPS 2015). ; 2015:595-603.
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2015 | Conference Paper | LibreCat-ID: 15406
Schäfer D, Hüllermeier E. 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. ; 2015:110-111.
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2015 | Journal Article | LibreCat-ID: 16067
Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.
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2015 | Conference Paper | LibreCat-ID: 319
Mohr F, Jungmann A, Kleine Büning H. Automated Online Service Composition. In: Proceedings of the 12th IEEE International Conference on Services Computing (SCC). ; 2015:57--64. doi:10.1109/SCC.2015.18
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2015 | Journal Article | LibreCat-ID: 4792
Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033. doi:10.1109/tfuzz.2015.2396078
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2015 | Conference Paper | LibreCat-ID: 10236
Abdel-Aziz A, Hüllermeier E. Case Base Maintenance in Preference-Based CBR. In: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015). ; 2015:1-14.
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2015 | Conference Paper | LibreCat-ID: 10243
El Mesaoudi-Paul A, Hüllermeier E. A CBR Approach to the Angry Birds Game. In: In Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015). ; 2015:68-77.
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2015 | Journal Article | LibreCat-ID: 10320
Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.
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2015 | Conference Paper | LibreCat-ID: 15750
Ewerth R, Balz A, Gehlhaar J, Dembczynski K, Hüllermeier E. Depth estimation in monocular images: Quantitative versus qualitative approaches. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany. KIT Scientific Publishing; 2015:235-240.
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2015 | Journal Article | LibreCat-ID: 16049
Senge R, Hüllermeier E. Fast fuzzy pattern tree learning for classification . IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033.
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2015 | Journal Article | LibreCat-ID: 16051
Hüllermeier E. From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions. Informatik Spektrum. 2015;38(6):500-509.
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2015 | Conference Paper | LibreCat-ID: 10237
Szörényi B, Busa-Fekete R, Weng P, Hüllermeier E. Qualitative Multi-Armed Bandits: A Quantile-Based Approach. In: In Proceedings International Conference on Machine Learning (ICML 2015). ; 2015:1660-1668.
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2015 | Conference Paper | LibreCat-ID: 10244
Schäfer D, Hüllermeier E. 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). ; 2015:110-111.
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2015 | Journal Article | LibreCat-ID: 10319
Waegeman W, Dembczynski K, Jachnik A, Cheng W, Hüllermeier E. On the Bayes-Optimality of F-Measure Maximizers. in Journal of Machine Learning Research. 2015;15:3333-3388.
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2015 | Journal Article | LibreCat-ID: 10321
Shaker A, Hüllermeier E. Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing. 2015;150:250-264.
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2015 | Conference Paper | LibreCat-ID: 15749
Paul A, Hüllermeier E. A cbr approach to the angry birds game. In: In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany. ; 2015:68-77.
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2015 | Conference Paper | LibreCat-ID: 15751
Lu S, Hüllermeier E. Locally weighted regression through data imprecisiation. In: Hoffmann F, Hüllermeier E, eds. In Proceedings 25th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2015:97-104.
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2015 | Journal Article | LibreCat-ID: 323
Jungmann A, Mohr F. An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications. 2015;(1):1-18. doi:10.1186/s13174-015-0022-8
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2015 | Conference Paper | LibreCat-ID: 10238
Schäfer D, Hüllermeier E. Dyad Ranking Using A Bilinear Plackett-Luce Model. In: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD). ; 2015:227-242.
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2015 | Conference Paper | LibreCat-ID: 10240
Henzgen S, Hüllermeier E. 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). ; 2015:422-437.
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2015 | Conference Paper | LibreCat-ID: 10245
Lu S, Hüllermeier E. Locally weighted regression through data imprecisiation. In: Proceedings 25. Workshop Computational Intelligence. ; 2015:97-104.
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2015 | Journal Article | LibreCat-ID: 10322
Hüllermeier E. From Knowledge-based to Data-driven fuzzy modeling-Development, criticism and alternative directions. Informatik Spektrum. 2015;38(6):500-509.
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2015 | Journal Article | LibreCat-ID: 16053
Hüllermeier E. Does machine learning need fuzzy logic? Fuzzy Sets and Systems. 2015;281:292-299.
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2015 | Journal Article | LibreCat-ID: 16058
Waegeman W, Dembczynski K, Jachnik A, Cheng W, Hüllermeier E. On the Bayes-optimality of F-measure maximizers. Journal of Machine Learning Research. 2015;15:3313-3368.
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2015 | Conference Paper | LibreCat-ID: 280
Arifulina S, Platenius MC, Mohr F, Engels G, Schäfer W. 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. ; 2015:333--340. doi:10.1109/SERVICES.2015.58
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2015 | Conference Paper | LibreCat-ID: 324
Mohr F. A Metric for Functional Reusability of Services. In: Proceedings of the 14th International Conference on Software Reuse (ICSR). LNCS. ; 2015:298--313. doi:10.1007/978-3-319-14130-5_21
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2015 | Journal Article | LibreCat-ID: 10323
Garcia-Jimenez S, Bustince U, Hüllermeier E, Mesiar R, Pal NR, Pradera A. Overlap Indices: Construction of and Application of Interpolative Fuzzy Systems. IEEE Transactions on Fuzzy Systems. 2015;23(4):1259-1273.
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2015 | Conference Paper | LibreCat-ID: 10234
Hüllermeier E, Minor M. Case-Based Reasoning Research and Development . In: In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343. Springer; 2015.
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2015 | Conference Paper | LibreCat-ID: 10239
Hüllermeier E, Cheng W. Superset Learning Based on Generalized Loss Minimization . In: In Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD). ; 2015:260-275.
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2015 | Conference Paper | LibreCat-ID: 10241
Szörényi B, Busa-Fekete R, Paul A, Hüllermeier E. Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. In: In Advances in Neural Information Processing Systems 28 (NIPS 2015). ; 2015:604-612.
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2015 | Conference Paper | LibreCat-ID: 10246
Ewerth R, Balz A, Gehlhaar J, Dembczynski K, Hüllermeier E. Depth estimation in monocular images: Quantitative versus qualitative approaches. In: Proceedings 25. Workshop Computational Intelligence. ; 2015:235-240.
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2014 | Journal Article | LibreCat-ID: 10297
Hoffmann F, Hüllermeier E, Kroll A. Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik. 2014;62(10):685-686.
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2014 | Journal Article | LibreCat-ID: 10312
Mernberger M, Moog M, Stork S, Zauner S, Maier UG, Hüllermeier E. Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J Bioinformatics and Computational Biology. 2014;12(1).
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2014 | Journal Article | LibreCat-ID: 10317
Krotzky T, Fober T, Hüllermeier E, Klebe G. Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Trans Comput Biology Bioinform. 2014;11(5):878-890.
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2014 | Conference Paper | LibreCat-ID: 10247
Busa-Fekete R, Szörényi B, Hüllermeier E. PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In: Proceedings AAAI 2014, Quebec, Canada. ; 2014:1701-1707.
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2014 | Conference Paper | LibreCat-ID: 10254
Calders T, Esposito F, Hüllermeier E, Meo R. Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In: Proceedings, Parts I-III. Lecture Notes in Computer Science. Springer; 2014:8724-8726.
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2014 | Journal Article | LibreCat-ID: 16079
Strickert M, Bunte K, Schleif FM, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.
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2014 | Conference Paper | LibreCat-ID: 10248
Busa-Fekete R, Hüllermeier E. A Survey of Preference-Based Online Learning with Bandit Algorithms. In: Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia. ; 2014:18-39.
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2014 | Conference Paper | LibreCat-ID: 10250
Fallah Tehrani A, Strickert M, Hüllermeier E. The Choquet kernel for monotone data. In: Proceedings ESANN , Bruges, Belgium. ; 2014.
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2014 | Journal Article | LibreCat-ID: 10298
Calders T, Esposito F, Hüllermeier E, Meo R. Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min Knowledge Discovery. 2014;28(5-6):1129-1133.
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2014 | Journal Article | LibreCat-ID: 10313
Calders T, Esposito F, Hüllermeier E, Meo R. Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning. 2014;97(1-2):1-3.
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2014 | Journal Article | LibreCat-ID: 10318
Stock M, Fober T, Hüllermeier E, et al. Identification of Functionally Releated Enzymes by Learning to Rank Methods. IEEE/ACM Trans Comput Biology Bioinform. 2014;11(6):1157-1169.
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2014 | Journal Article | LibreCat-ID: 16082
Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.
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2014 | Conference Paper | LibreCat-ID: 353
Mohr F, Walther S. Template-based Generation of Semantic Services. In: Proceedings of the 14th International Conference on Software Reuse (ICSR). LNCS. ; 2014:188-203. doi:10.1007/978-3-319-14130-5_14
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2014 | Conference Paper | LibreCat-ID: 428
Mohr F. Estimating Functional Reusability of Services. In: Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC). LNCS. ; 2014:411-418.
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2014 | Conference Paper | LibreCat-ID: 447
Jungmann A, Mohr F, Kleinjohann B. Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. In: Proceedings of the 10th World Congress on Services (SERVICES). ; 2014:346-353. doi:10.1109/SERVICES.2014.68
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2014 | Conference Paper | LibreCat-ID: 10249
Henzgen S, Hüllermeier E. Mining Rank Data. In: Proceedings Discovery Science, Bled,Slovenia . ; 2014:123-134.
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2014 | Conference Paper | LibreCat-ID: 10251
Abdel-Aziz A, Strickert M, Hüllermeier E. Learning Solution Similarity in Preference-Based CBR. In: Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland. ; 2014:17-31.
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2014 | Journal Article | LibreCat-ID: 10299
Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy rule-based systems. Evolving Systems. 2014;5(3):175-191.
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2014 | Journal Article | LibreCat-ID: 10314
Busa-Fekete R, Szörényi B, Weng P, Cheng W, Hüllermeier E. Preference-Based Reinforcement Learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning. 2014;97(3):327-351.
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2014 | Journal Article | LibreCat-ID: 16064
Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning. 2014;55(7):1519-1534.
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2014 | Journal Article | LibreCat-ID: 16069
Henzgen S, Strickert M, Hüllermeier E. Visualization of evolving fuzzy-rule-based systems. Evolving Systems. 2014;5:175-191.
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2014 | Journal Article | LibreCat-ID: 16083
Donner-Banzhoff N, Haasenritter J, Hüllermeier E, Viniol A, Bösner S, Becker A. The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology. 2014;2(67):124-132.
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2014 | Conference Paper | LibreCat-ID: 10295
Fürnkranz J, Hüllermeier E, Rudin C, Slowinski R, Sanner S. Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports. In: Vol 4. ; 2014:1-27.
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2014 | Journal Article | LibreCat-ID: 10308
Hüllermeier E. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int J Approx Reasoning. 2014;55(7):1519-1534.
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2014 | Journal Article | LibreCat-ID: 10310
Strickert M, Bunte K, Schleif F-M, Hüllermeier E. Correlation-based embedding of pairwise score data. Neurocomputing. 2014;141:97-109.
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2014 | Journal Article | LibreCat-ID: 10315
Montanés E, Senge R, Barranquero J, Quevedo JR, Del Coz JJ, Hüllermeier E. Dependent binary relevance models for multi-label classification. Pattern Recognition. 2014;47(3):1494-1508.
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2014 | Journal Article | LibreCat-ID: 16046
Agarwal M, Fallah Tehrani A, Hüllermeier E. Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis. 2014;22(3-4).
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2014 | Journal Article | LibreCat-ID: 16060
Krotzky T, Fober T, Hüllermeier E, Klebe G. Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics. 2014;11(5):878-890.
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2014 | Journal Article | LibreCat-ID: 16077
Busa-Fekete R, Szörenyi B, Weng P, Cheng W, Hüllermeier E. Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning. 2014;97(3):327-351.
LibreCat
 

2014 | Journal Article | LibreCat-ID: 10296
Shaker A, Hüllermeier E. Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science. 2014;24(1):199-212.
LibreCat
 

2014 | Journal Article | LibreCat-ID: 10309
Hüllermeier E. Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int J Approx Reasoning. 2014;55(7):1609-1613.
LibreCat
 

2014 | Journal Article | LibreCat-ID: 10311
Senge R, Bösner S, Dembczynski K, et al. Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences. 2014;255:16-29.
LibreCat
 

2014 | Journal Article | LibreCat-ID: 10316
Krempl G, Zliobaite I, Brzezinski D, et al. Open challenges for data stream mining research. SIGKDD Explorations. 2014;16(1):1-10.
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