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1909 Publications


2014 | Journal Article | LibreCat-ID: 10169
Hägglund, J., & Steffen, E. (2014). Petersen colorings and some families of snarks. Ars Mathematica Contemporanea, 312, 476–478.
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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 | Dissertation | LibreCat-ID: 10291
Herrmann, P. (2014). On Consumer Purchasing Behavior in Electronic Markets. Universität Paderborn.
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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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