@inproceedings{98a5f02459d74e5c884a9696e20cb526,
title = "Margin-based ranking meets boosting in the middle",
abstract = "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.",
author = "Cynthia Rudin and Corinna Cortes and Mehryar Mohri and Schapire, {Robert E.}",
year = "2005",
doi = "10.1007/11503415_5",
language = "English (US)",
isbn = "3540265562",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "63--78",
booktitle = "Learning Theory - 18th Annual Conference on Learning Theory, COLT 2005, Proceedings",
note = "18th Annual Conference on Learning Theory, COLT 2005 - Learning Theory ; Conference date: 27-06-2005 Through 30-06-2005",
}