A general model is proposed for studying ranking problems. We investigate learning methods based on empirical minimization of the natural estimates of the ranking risk. The empiric...
We are interested in supervised ranking with the following twist: our goal is to design algorithms that perform especially well near the top of the ranked list, and are only requir...
We consider the general, widely applicable problem of selecting from n real-valued random variables a subset of size m of those with the highest means, based on as few samples as ...
Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches. In such a SVM active learning environment, the ...
This paper studies the complexity of learning classes of expressions in propositional logic from equivalence queries and membership queries. In particular, we focus on bounding th...
Marta Arias, Aaron Feigelson, Roni Khardon, Rocco ...