Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learning Transformation Models for Ranking and Survival Analysis
The Journal of Machine Learning Research
Prototype based modelling for ordinal classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidate to study ranking models and ordinal regression in a context of machine learning. We do implement a structural risk minimization strategy based on a Lipschitz smoothness condition of the transformation model. Then, it is shown how the estimate can be obtained efficiently by solving a convex quadratic program with O (n ) linear constraints and unknowns, with n the number of data points. A set of experiments do support these findings.