Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
Journal of Combinatorial Theory Series A
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
On the rate of convergence of regularized boosting classifiers
The Journal of Machine Learning Research
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
The Journal of Machine Learning Research
Robust reductions from ranking to classification
Machine Learning
The Journal of Machine Learning Research
An Unsupervised Learning Algorithm for Rank Aggregation
ECML '07 Proceedings of the 18th European conference on Machine Learning
Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
An efficient algorithm for learning to rank from preference graphs
Machine Learning
IEEE Transactions on Information Theory
MINLIP: Efficient Learning of Transformation Models
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
The Journal of Machine Learning Research
CCRM: an effective algorithm for mining commodity information from threaded Chinese customer reviews
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Robust reductions from ranking to classification
COLT'07 Proceedings of the 20th annual conference on Learning theory
Learning Transformation Models for Ranking and Survival Analysis
The Journal of Machine Learning Research
Multiview semi-supervised learning for ranking multilingual documents
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Machine learning ranking and INEX’05
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
On ranking and generalization bounds
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning theory approach to minimum error entropy criterion
The Journal of Machine Learning Research
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A general model is proposed for studying ranking problems. We investigate learning methods based on empirical minimization of the natural estimates of the ranking risk. The empirical estimates are of the form of a U-statistic. Inequalities from the theory of U-statistics and U-processes are used to obtain performance bounds for the empirical risk minimizers. Convex risk minimization methods are also studied to give a theoretical framework for ranking algorithms based on boosting and support vector machines. Just like in binary classification, fast rates of convergence are achieved under certain noise assumption. General sufficient conditions are proposed in several special cases that guarantee fast rates of convergence.