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444 Publications


2016 | Conference Paper | LibreCat-ID: 15404
Plackett-Luce networks for dyad ranking
D. Schäfer, E. Hüllermeier, in: In Workshop LWDA “Lernen, Wissen, Daten, Analysen” Potsdam, Germany, 2016.
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2016 | Conference Paper | LibreCat-ID: 15111
Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts
K. Pfannschmidt, E. Hüllermeier, S. Held, R. Neiger, in: In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–461.
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2016 | Journal Article | LibreCat-ID: 16041
CavSimBase: A database for large scale comparison of protein binding sites
M. Leinweber, T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, E. Hüllermeier, IEEE Transactions on Knowledge and Data Engineering 28 (2016) 1423–1434.
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2016 | Book Chapter | LibreCat-ID: 10214
Preference Learning
J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2016.
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2016 | Conference (Editor) | LibreCat-ID: 10221
Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany
F. Hoffmann, E. Hüllermeier, R. Mikut, eds., Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany, 2016.
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2016 | Conference Paper | LibreCat-ID: 10222
Extreme F-measure maximization using sparse probability estimates
K. Jasinska, K. Dembczynski, R. Busa-Fekete, T. Klerx, E. Hüllermeier, in: M.F. Balcan, K.Q. Weinberger (Eds.), Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA, 2016.
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2016 | Conference Paper | LibreCat-ID: 10223
Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016
V. Melnikov, E. Hüllermeier, in: European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva Del Garda, Italy, 2016, pp. 756–771.
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2016 | Conference Paper | LibreCat-ID: 10224
Consistency of probalistic classifier trees
K. Dembczynski, W. Kotlowski, W. Waegeman, R. Busa-Fekete, E. Hüllermeier, 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: 10225
Predicting the electricity consumption of buildings: An improved CBR approach
A. Shabani, A. Paul, R. Platon, E. Hüllermeier, in: In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA, 2016, pp. 356–369.
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2016 | Conference Paper | LibreCat-ID: 10226
Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts
K. Pfannschmidt, E. Hüllermeier, S. Held, R. Neiger, in: In Proceedings IPMU 16th International Conference on Information Processing and Management  of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands, Springer, 2016, pp. 450–461.
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2016 | Conference Paper | LibreCat-ID: 10227
On the Identifiability of models in multi-criteria preference learning
C. Labreuche, E. Hüllermeier, P. Vojtas, A. Fallah Tehrani, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.
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2016 | Conference Paper | LibreCat-ID: 10228
Preference-Based Reinforcement Learning Using Dyad Ranking
D. Schäfer, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.
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2016 | Conference Paper | LibreCat-ID: 10229
Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators
I. Couso, M. Ahmadi Fahandar, E. Hüllermeier, in: R. Busa-Fekete, E. Hüllermeier, V. Mousseau, K. Pfannschmidt (Eds.), Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning, 2016.
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2016 | Conference Paper | LibreCat-ID: 10230
Support vector classification on noisy data using fuzzy supersets losses
S. Lu, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing, 2016, pp. 1–8.
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2016 | Conference Paper | LibreCat-ID: 10231
Plackett-Luce networks for dyad ranking
D. Schäfer, E. Hüllermeier, in: In Workshop LWDA “Lernen, Wissen, Daten, Analysen,” 2016.
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2016 | Conference (Editor) | LibreCat-ID: 10263
ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence
G.A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, F. van Harmelen, eds., ECAI 2016, 22nd European Conference on Artificial Intelligence, Including PAIS 2016, Prestigious Applications of Artificial Intelligence, IOS Press, The Hague, The Netherlands, 2016.
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2016 | Journal Article | LibreCat-ID: 10264
CavSimBase: A database for large scale comparison of protein binding sites
M. Leinweber, T. Fober, M. Strickert, L. Baumgärtner, G. Klebe, B. Freisleben, E. Hüllermeier, IEEE Transactions on Knowledge and Data Engineering 28 (2016) 1423–1434.
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2016 | Journal Article | LibreCat-ID: 10266 LibreCat
 

2015 | Journal Article | LibreCat-ID: 4792
Fast Fuzzy Pattern Tree Learning for Classification
R. Senge, E. Hüllermeier, IEEE Transactions on Fuzzy Systems 23 (2015) 2024–2033.
LibreCat | Files available | DOI
 

2015 | Conference Paper | LibreCat-ID: 15406
Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations
D. Schäfer, E. Hüllermeier, 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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