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...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
MIT Press
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/52342 |