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428 Publications


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 | 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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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: 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: 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: 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 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: 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 | 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 (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 | 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 | 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 | 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: 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 | 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 | Dissertation | LibreCat-ID: 141
F. Mohr, Towards Automated Service Composition Under Quality Constraints. Universität Paderborn, 2016.
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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 | 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 | 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 | 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: 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: 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 | 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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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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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: 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: 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 | 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: 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: 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: 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 | 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 | 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 | 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 | 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: 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 | 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: 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 | 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: 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: 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: 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: 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: 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 | 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: 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 | 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 | 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 | 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 | 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: 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: 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: 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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2014 | Journal Article | LibreCat-ID: 10297
F. Hoffmann, E. Hüllermeier, and A. Kroll, “Ausgewählte Beiträge des GMA-Fachausschusses 5.14,” Computational Intelligence Automatisierungstechnik, vol. 62, no. 10, pp. 685–686, 2014.
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2014 | Journal Article | LibreCat-ID: 10312
M. Mernberger, M. Moog, S. Stork, S. Zauner, U. G. Maier, and E. Hüllermeier, “Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances,” J. Bioinformatics and Computational Biology, vol. 12, no. 1, 2014.
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2014 | Journal Article | LibreCat-ID: 10317
T. Krotzky, T. Fober, E. Hüllermeier, and G. Klebe, “Extended Graph-Based Models for Enhanced Similarity Search in Cavbase,” IEEE/ACM Trans. Comput. Biology Bioinform., vol. 11, no. 5, pp. 878–890, 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: 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.
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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 | 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: 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 | Journal Article | LibreCat-ID: 10298
T. Calders, F. Esposito, E. Hüllermeier, and R. Meo, “Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track,” Data Min. Knowledge Discovery, vol. 28, no. 5–6, pp. 1129–1133, 2014.
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2014 | Journal Article | LibreCat-ID: 10313
T. Calders, F. Esposito, E. Hüllermeier, and R. Meo, “Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track,” Machine Learning, vol. 97, no. 1–2, pp. 1–3, 2014.
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2014 | Journal Article | LibreCat-ID: 10318
M. Stock et al., “Identification of Functionally Releated Enzymes by Learning to Rank Methods,” IEEE/ACM Trans. Comput. Biology Bioinform., vol. 11, no. 6, pp. 1157–1169, 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 | 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: 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 | 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: 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: 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.
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2014 | Journal Article | LibreCat-ID: 10299
S. Henzgen, M. Strickert, and E. Hüllermeier, “Visualization of evolving fuzzy rule-based systems,” Evolving Systems, vol. 5, no. 3, pp. 175–191, 2014.
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2014 | Journal Article | LibreCat-ID: 10314
R. Busa-Fekete, B. Szörényi, 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: 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: 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: 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.
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2014 | Journal Article | LibreCat-ID: 10308
E. Hüllermeier, “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization,” Int. J. Approx. Reasoning, vol. 55, no. 7, pp. 1519–1534, 2014.
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2014 | Journal Article | LibreCat-ID: 10310
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: 10315
E. Montanés, R. Senge, J. Barranquero, J. R. Quevedo, J. J. Del Coz, and E. Hüllermeier, “Dependent binary relevance models for multi-label classification,” Pattern Recognition, vol. 47, no. 3, pp. 1494–1508, 2014.
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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: 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: 10296
A. Shaker and E. Hüllermeier, “Survival analysis on data streams: Analyzing temporal events in dynamically changing environments,” Applied Mathematics and Computer Science, vol. 24, no. 1, pp. 199–212, 2014.
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2014 | Journal Article | LibreCat-ID: 10309
E. Hüllermeier, “Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization,” Int. J. Approx. Reasoning, vol. 55, no. 7, pp. 1609–1613, 2014.
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2014 | Journal Article | LibreCat-ID: 10311
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: 10316
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 | 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.
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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: 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 | 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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2013 | Conference Paper | LibreCat-ID: 13190
A. Shaker and E. Hüllermeier, “Recovery analysis for adaptive learning from non-stationary data streams,” in in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, 2013, pp. 289–298.
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2013 | Conference Paper | LibreCat-ID: 13119
S. Henzgen, M. Strickert, and E. Hüllermeier, “Rule chains for visualizing evolving fuzzy rule-based systems,” in in Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, Wroclaw, Poland, 2013, pp. 279–288.
LibreCat
 

2013 | Journal Article | LibreCat-ID: 16081
S. Bösner, K. Bönisch, J. Haasenritter , P. Schlegel, E. Hüllermeier, and N. Donner-Banzhoff, “Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? A cross sectional study. ,” BMC Family Practice, vol. 14, no. 1, pp. 154–162, 2013.
LibreCat
 

2013 | Journal Article | LibreCat-ID: 16086
J. Haasenritter et al., “Diagnose im Kontext - eine erweiterte Perspektive,” Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen (ZEFQ), vol. 107, pp. 585–591, 2013.
LibreCat
 

2013 | Conference Paper | LibreCat-ID: 15759
W. Cheng and E. Hüllermeier, “A nearest neigbor approach to label ranking based on generalized labelwise loss minimization,” in In Proceedings M-PREF`13, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China, 2013.
LibreCat
 

2013 | Conference Paper | LibreCat-ID: 15761
R. Senge, J. J. del Coz, and E. Hüllermeier, “On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery,” in In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany , 2013.
LibreCat
 

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