Margin-based Ranking and an Equivalence between AdaBoost and RankBoost

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost

We study boosting algorithms for learning to rank. We give a general margin-based bound for ranking based on covering numbers for the hypothesis space. Our bound suggests that algorithms that maximize the ranking margin will generalize well. We then describe a new algorithm, smooth margin ranking...

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Bibliographic Details
Main Authors: Rudin, Cynthia, Schapire, Robert E.
Other Authors: Sloan School of Management
Format: Article
Language:en_US
Published: MIT Press 2010
Subjects:
area under the ROC curve
AdaBoost
generalization bounds
RankBoost
ranking
Online Access:http://hdl.handle.net/1721.1/52342

Internet

http://hdl.handle.net/1721.1/52342

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