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
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Margin-Based ranking meets boosting in the middle
COLT'05 Proceedings of the 18th annual conference on Learning Theory
ExpertRank: A topic-aware expert finding algorithm for online knowledge communities
Decision Support Systems
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The method we present aims at building a weighted linear combination of already trained dichotomizers, where the weights are determined to maximize the minimum rank margin of the resulting ranking system. This is particularly suited for real applications where it is difficult to exactly determine key parameters such as costs and priors. In such cases ranking is needed rather than classification. A ranker can be seen as a more basic system than a classifier since it ranks the samples according to the value assigned by the classifier to each of them. Experiments on popular benchmarks along with a comparison with other typical rankers are proposed to show how effective can be the approach.