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449 Publications


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.
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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.
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2013 | Journal Article | LibreCat-ID: 16123
A. Shaker, R. Senge, and E. Hüllermeier, “Evolving fuzzy pattern trees for binary classification on data streams,” Information Sciences, vol. 220, pp. 34–45, 2013.
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2013 | Conference Paper | LibreCat-ID: 13115
G. Szarvas, R. Busa-Fekete, and E. Hüllermeier, “Learning to rank lexical substitutions,” in In Proceedings EMNLP-2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA, 2013.
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2013 | Conference Paper | LibreCat-ID: 13116
K. Dembczynski, A. Jachnik, W. Kotlowski, W. Waegeman, and E. Hüllermeier, “Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization,” in in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, 2013, pp. 1130–1138.
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2013 | Conference Paper | LibreCat-ID: 13117
R. Busa-Fekete, B. Szoreny, P. Weng, W. Cheng, and E. Hüllermeier, “Top-k selection based on adaptive sampling of noisy preferences,” in in Proceedings ICML-2013, 30th International Conference on Machine Learning, Atlanta, USA, 2013, pp. 1094–1102.
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2013 | Conference Paper | LibreCat-ID: 13118
E. Hüllermeier and W. Cheng, “Preference-based CBR: General ideas and basic principles,” in in Proceedings IJCAI-13, 23rd international Joint Conference on Artificial Intelligence, Beijing, China, 2013, pp. 3012–3016.
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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.
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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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2012 | Conference Paper | LibreCat-ID: 15299
M. Leinweber et al., “GPU-based cloud computing for comparing the structure of protein binding sites,” in in Proceedings IEEE Conference on Digital Ecosystem Technologies-Complex Environment Engineering Campione d`Italia, Italy, 2012.
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2012 | Book Chapter | LibreCat-ID: 15396
E. Hüllermeier and A. Fallah Tehrani, “Efficient learning of classifiers based on the 2-additive Choquet integral,” in Computational Intelligence in Intelligent Data Analysis, C. Moewes and A. Nürnberger, Eds. Springer, 2012, pp. 17–30.
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2012 | Conference Paper | LibreCat-ID: 15754
M. Bräuning and E. Hüllermeier, “Learning conditional lexicographic preference trees,” in In Workshops on Preference Learning at ECAI, European Conference on Artiticial intelligence, Montpellier, France, 2012.
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2012 | Conference Paper | LibreCat-ID: 15114
E. Hüllermeier and A. Fallah Tehrani, “On the VC dimension of the Choquet integral,” in In Proceedings IPMU-2012 14th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Catania, Italy, 2012.
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2012 | Journal Article | LibreCat-ID: 16084
A. Fallah Tehrani, W. Cheng, K. Dembczynski, and E. Hüllermeier, “Learning  monotone nonlinear models using the Choquet integral,” Machine Learning, vol. 89, no. 1, pp. 183–211, 2012.
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2012 | Journal Article | LibreCat-ID: 16085
H. Bustince, M. Pagola, R. Mesiar, E. Hüllermeier, and F. Herrera, “Grouping, overlap and generalized bientropic functions for fuzzy modeling of pairwise comparisons,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 3, pp. 405–415, 2012.
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2012 | Journal Article | LibreCat-ID: 16087
J. Fürnkranz, E. Hüllermeier, W. Cheng, and S. H. Park, “Preference-based reinforcement learning: A formal framework and a policy iteration algorithm,” Machine Learning, vol. 89, no. 1, pp. 123–156, 2012.
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2012 | Journal Article | LibreCat-ID: 16088
K. Dembczynski, W. Waegeman, W. Cheng, and E. Hüllermeier, “On label dependence and loss  minimization in multi-label classification,” Machine Learning, vol. 88, no. 1–2, pp. 5–45, 2012.
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2012 | Journal Article | LibreCat-ID: 16089
A. Shaker and E. Hüllermeier, “IBL Streams: A system for instance-based classification and regression on data streams,” Evolving Systems, vol. 3, no. 4, pp. 235–249, 2012.
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2012 | Journal Article | LibreCat-ID: 16090
M. Dolorez Ruiz and E. Hüllermeier, “A formal and empirical analysis of the fuzzy gamma rank correlation coefficient,” Information Sciences, vol. 206, pp. 1–17, 2012.
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2012 | Journal Article | LibreCat-ID: 16091
A. Fallah Tehrani, W. Cheng, and E. Hüllermeier, “Preference learning using the Choquet integral: The case of multipartite ranking,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, pp. 1102–1113, 2012.
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