- Supervised Learning as Preference Optimization: Recent Applications
F. Aiolli and A. Sperduti
- Integrating Expert Knowledge into Kernel-Based Preference Models
W. Waegeman, B. De Baets, and L. Boullart
- Learning Preference Relations over Combinatorial Domains
J. Lang and J. Mengin
- Learning Preferences with Co-Regularized Least-Squares
E. Tsivtsivadze, F. Gieseke, T. Pahikkala, J. Boberg, and T. Salakoski
- Choice Based Conjoint Analysis: Discrete Choice Models vs. Direct Regression
B. Taneva, J. Giesen, P. Zolliker, and K. Mueller
- Discovering Relevant Preferences in a Personalised Recommender System using Machine Learning Techniques
A. Bellogín, I. Cantador, P. Castells, and A. Ortigosa
- Learning SVM Ranking Function from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain
R. Arens
- Learning to Rank Cases with Classification Rules
J. Zhang, J.W. Bala, A. Hadjarian, and B. Han
- Statistical Approach to Ordinal Classification with Monotonicity Constraints
W. Kotlowski and R. Slowinski
- Analyzing Ranking Data Using Decision Tree
P.L.H. Yu, W.M. Wan, and P.H. Lee
- Multi-Label Classification With Contraints
S. Park and J. Fuernkranz
- Instance-based label ranking using the Mallows model
W. Cheng and E. Huellermeier
Note: ECML PKDD workshop papers should be considered informal technical
reports describing ongoing research. They are not to be considered papers at
the main ECML PKDD conference, and have not gone through the main ECML PKDD
reviewing procedure.
