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449 Publications


2014 | Journal Article | LibreCat-ID: 16082
Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., & Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
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2014 | Journal Article | LibreCat-ID: 16083
Donner-Banzhoff, N., Haasenritter, J., Hüllermeier, E., Viniol, A., Bösner, S., & Becker, A. (2014). The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2(67), 124–132.
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2014 | Conference Paper | LibreCat-ID: 10247
Busa-Fekete, R., Szörényi, B., & Hüllermeier, E. (2014). PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. In Proceedings AAAI 2014, Quebec, Canada (pp. 1701–1707).
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2014 | Conference Paper | LibreCat-ID: 10248
Busa-Fekete, R., & Hüllermeier, E. (2014). A Survey of Preference-Based Online Learning with Bandit Algorithms. In Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia (pp. 18–39).
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2014 | Conference Paper | LibreCat-ID: 10249
Henzgen, S., & Hüllermeier, E. (2014). Mining Rank Data. In Proceedings Discovery Science, Bled,Slovenia (pp. 123–134).
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2014 | Conference Paper | LibreCat-ID: 10250
Fallah Tehrani, A., Strickert, M., & Hüllermeier, E. (2014). The Choquet kernel for monotone data. In Proceedings ESANN , Bruges, Belgium.
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2014 | Conference Paper | LibreCat-ID: 10251
Abdel-Aziz, A., Strickert, M., & Hüllermeier, E. (2014). Learning Solution Similarity in Preference-Based CBR. In Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland (pp. 17–31).
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2014 | Conference Paper | LibreCat-ID: 10253
Schäfer, D., & Hüllermeier, E. (2014). Dyad Ranking Using A Bilinear Plackett-Luce Model. In Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany (pp. 32–33).
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2014 | Conference Paper | LibreCat-ID: 10254
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France. In Proceedings, Parts I-III. Lecture Notes in Computer Science (pp. 8724–8726). Springer.
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2014 | Conference Paper | LibreCat-ID: 10295
Fürnkranz, J., Hüllermeier, E., Rudin, C., Slowinski, R., & Sanner, S. (2014). Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports (Vol. 4, pp. 1–27).
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2014 | Journal Article | LibreCat-ID: 10296
Shaker, A., & Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Applied Mathematics and Computer Science, 24(1), 199–212.
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2014 | Journal Article | LibreCat-ID: 10297
Hoffmann, F., Hüllermeier, E., & Kroll, A. (2014). Ausgewählte Beiträge des GMA-Fachausschusses 5.14. Computational Intelligence Automatisierungstechnik, 62(10), 685–686.
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2014 | Journal Article | LibreCat-ID: 10298
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Data Min. Knowledge Discovery, 28(5–6), 1129–1133.
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2014 | Journal Article | LibreCat-ID: 10299
Henzgen, S., Strickert, M., & Hüllermeier, E. (2014). Visualization of evolving fuzzy rule-based systems. Evolving Systems, 5(3), 175–191.
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2014 | Journal Article | LibreCat-ID: 10308
Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1519–1534.
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2014 | Journal Article | LibreCat-ID: 10309
Hüllermeier, E. (2014). Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning, 55(7), 1609–1613.
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2014 | Journal Article | LibreCat-ID: 10310
Strickert, M., Bunte, K., Schleif, F.-M., & Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
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2014 | Journal Article | LibreCat-ID: 10311
Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., & Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
LibreCat
 

2014 | Journal Article | LibreCat-ID: 10312
Mernberger, M., Moog, M., Stork, S., Zauner, S., Maier, U. G., & Hüllermeier, E. (2014). Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J. Bioinformatics and Computational Biology, 12(1).
LibreCat
 

2014 | Journal Article | LibreCat-ID: 10313
Calders, T., Esposito, F., Hüllermeier, E., & Meo, R. (2014). Guest editors`introduction:special issue of the ECML/PKDD 2014 journal track. Machine Learning, 97(1–2), 1–3.
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