Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A few notes on statistical learning theory
Advanced lectures on machine learning
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
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
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
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
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Improving the sample complexity using global data
IEEE Transactions on Information Theory
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The problem of ranking is to predict or to guess the ordering between objects on the basis of their observed features. In this paper we consider ranking estimators that minimize the empirical convex risk. We prove generalization bounds for the excess risk of such estimators with rates that are faster than 1/√n. We apply our results to commonly used ranking algorithms, for instance boosting or support vector machines. Moreover, we study the performance of considered estimators on real data sets.