Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
The Journal of Machine Learning Research
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
The Journal of Machine Learning Research
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
Maximizing the area under the ROC curve by pairwise feature combination
Pattern Recognition
Robust reductions from ranking to classification
Machine Learning
ACM SIGACT News
The Journal of Machine Learning Research
ECML '07 Proceedings of the 18th European conference on Machine Learning
Hinge Rank Loss and the Area Under the ROC Curve
ECML '07 Proceedings of the 18th European conference on Machine Learning
An Unsupervised Learning Algorithm for Rank Aggregation
ECML '07 Proceedings of the 18th European conference on Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Smooth Boosting for Margin-Based Ranking
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
The Journal of Machine Learning Research
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
The Journal of Machine Learning Research
An alternative ranking problem for search engines
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Robust reductions from ranking to classification
COLT'07 Proceedings of the 20th annual conference on Learning theory
A linear combination of classifiers via rank margin maximization
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Learning to rank using 1-norm regularization and convex hull reduction
Proceedings of the 48th Annual Southeast Regional Conference
Learning a Robust Relevance Model for Search Using Kernel Methods
The Journal of Machine Learning Research
Adding smarter systems instead of human annotators: re-ranking for system combination
Proceedings of the 1st international workshop on Search and mining entity-relationship data
Large-Margin thresholded ensembles for ordinal regression: theory and practice
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
ECML'06 Proceedings of the 17th European conference on Machine Learning
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Learning bilinear model for matching queries and documents
The Journal of Machine Learning Research
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We present several results related to ranking. We give a general margin-based bound for ranking based on the L∞ covering number of the hypothesis space. Our bound suggests that algorithms that maximize the ranking margin generalize well. We then describe a new algorithm, Smooth Margin Ranking, that precisely converges to a maximum ranking-margin solution. The algorithm is a modification of RankBoost, analogous to Approximate Coordinate Ascent Boosting. We also prove a remarkable property of AdaBoost: under very natural conditions, AdaBoost maximizes the exponentiated loss associated with the AUC and achieves the same AUC as RankBoost. This explains the empirical observations made by Cortes and Mohri, and Caruana and Niculescu-Mizil, about the excellent performance of AdaBoost as a ranking algorithm, as measured by the AUC.