Browsing by Subject "generalization bounds"
Now showing items 1-2 of 2
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Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
(MIT Press, 2009-10)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 ... -
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
(MIT Press, 2009-10)We are interested in supervised ranking algorithms that perform especially well near the top of the ranked list, and are only required to perform sufficiently well on the rest of the list. In this work, we provide a ...