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Johannes Fürnkranz
- > Home > Persons > Johannes Fürnkranz
Publications
- 2023
- [c132]Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier:
Probabilistic Scoring Lists for Interpretable Machine Learning. DS 2023: 189-203 - 2022
- [j45]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp:
A flexible class of dependence-aware multi-label loss functions. Mach. Learn. 111(2): 713-737 (2022) - 2021
- [c117]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-Based Label Binning in Multi-label Classification. ECML/PKDD (3) 2021: 462-477 - [i27]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-based Label Binning in Multi-label Classification. CoRR abs/2106.11690 (2021) - 2020
- [c115]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. DS 2020: 533-547 - [c113]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. KI 2020: 290-296 - [c112]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. ECML/PKDD (3) 2020: 124-140 - [c111]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen, Michael Rapp:
Rule-Based Multi-label Classification: Challenges and Opportunities. RuleML+RR 2020: 3-19 - [i23]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. CoRR abs/2006.11916 (2020) - [i22]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. CoRR abs/2006.13346 (2020) - [i21]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. CoRR abs/2007.08145 (2020) - [i20]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp:
A Flexible Class of Dependence-aware Multi-Label Loss Functions. CoRR abs/2011.00792 (2020) - [i19]Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Michael Rapp:
Learning Structured Declarative Rule Sets - A Challenge for Deep Discrete Learning. CoRR abs/2012.04377 (2020) - 2018
- [i5]Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, Michael Rapp:
Learning Interpretable Rules for Multi-label Classification. CoRR abs/1812.00050 (2018) - 2017
- [r12]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1000-1005 - [r10]Johannes Fürnkranz, Eyke Hüllermeier:
Rank Correlation. Encyclopedia of Machine Learning and Data Mining 2017: 1055 - 2016
- [j33]Johannes Fürnkranz, Eyke Hüllermeier:
Special Issue on Discovery Science. Inf. Sci. 329: 849-850 (2016) - 2014
- [i3]Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, Scott Sanner:
Preference Learning (Dagstuhl Seminar 14101). Dagstuhl Reports 4(3): 1-27 (2014) - 2013
- [j29]Eyke Hüllermeier, Johannes Fürnkranz:
Editorial: Preference learning and ranking. Mach. Learn. 93(2-3): 185-189 (2013) - [e6]Johannes Fürnkranz, Eyke Hüllermeier, Tomoyuki Higuchi:
Discovery Science - 16th International Conference, DS 2013, Singapore, October 6-9, 2013. Proceedings. Lecture Notes in Computer Science 8140, Springer 2013, ISBN 978-3-642-40896-0 [contents] - 2012
- [j27]Johannes Fürnkranz, Eyke Hüllermeier, Weiwei Cheng, Sang-Hyeun Park:
Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. Mach. Learn. 89(1-2): 123-156 (2012) - 2011
- [c59]Eyke Hüllermeier, Johannes Fürnkranz:
Learning from Label Preferences. ALT 2011: 38 - [c58]Eyke Hüllermeier, Johannes Fürnkranz:
Learning from Label Preferences. Discovery Science 2011: 2-17 - [c54]Weiwei Cheng, Johannes Fürnkranz, Eyke Hüllermeier, Sang-Hyeun Park:
Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning. ECML/PKDD (1) 2011: 312-327 - 2010
- [j22]Eyke Hüllermeier, Johannes Fürnkranz:
On predictive accuracy and risk minimization in pairwise label ranking. J. Comput. Syst. Sci. 76(1): 49-62 (2010) - [p5]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning: An Introduction. Preference Learning 2010: 1-17 - [p4]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning and Ranking by Pairwise Comparison. Preference Learning 2010: 65-82 - [e5]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Springer 2010, ISBN 978-3-642-14124-9 [contents] - [r3]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Encyclopedia of Machine Learning 2010: 789-795 - 2009
- [c42]Johannes Fürnkranz, Eyke Hüllermeier, Stijn Vanderlooy:
Binary Decomposition Methods for Multipartite Ranking. ECML/PKDD (1) 2009: 359-374 - 2008
- [j19]Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng, Klaus Brinker:
Label ranking by learning pairwise preferences. Artif. Intell. 172(16-17): 1897-1916 (2008) - [j17]Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker:
Multilabel classification via calibrated label ranking. Mach. Learn. 73(2): 133-153 (2008) - 2007
- [c34]Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier:
On Pairwise Naive Bayes Classifiers. ECML 2007: 371-381 - [c33]Eyke Hüllermeier, Johannes Fürnkranz:
On Minimizing the Position Error in Label Ranking. ECML 2007: 583-590 - 2006
- [c28]Klaus Brinker, Johannes Fürnkranz, Eyke Hüllermeier:
A Unified Model for Multilabel Classification and Ranking. ECAI 2006: 489-493 - 2005
- [j15]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Künstliche Intell. 19(1): 60- (2005) - [c25]Eyke Hüllermeier, Johannes Fürnkranz:
Learning Label Preferences: Ranking Error Versus Position Error. IDA 2005: 180-191 - [c24]Eyke Hüllermeier, Johannes Fürnkranz, Jürgen Beringer:
On Position Error and Label Ranking through Iterated Choice. LWA 2005: 158-163 - 2004
- [c21]Eyke Hüllermeier, Johannes Fürnkranz:
Ranking by pairwise comparison a note on risk minimization. FUZZ-IEEE 2004: 97-102 - 2003
- [c19]Johannes Fürnkranz, Eyke Hüllermeier:
Pairwise Preference Learning and Ranking. ECML 2003: 145-156
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