Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
Learning Coordinate Covariances via Gradients
The Journal of Machine Learning Research
Generalization Bounds for Ranking Algorithms via Algorithmic Stability
The Journal of Machine Learning Research
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
Application of integral operator for regularized least-square regression
Mathematical and Computer Modelling: An International Journal
Statistical Analysis of Bayes Optimal Subset Ranking
IEEE Transactions on Information Theory
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In this paper, we investigate the generalization performance of a regularized ranking algorithm in a reproducing kernel Hilbert space associated with least square ranking loss. An explicit expression for the solution via a sampling operator is derived and plays an important role in our analysis. Convergence analysis for learning a ranking function is provided, based on a novel capacity independent approach, which is stronger than for previous studies of the ranking problem.