We investigate algebraic, logical, and geometric properties of concepts recognized by various classes of probabilistic classifiers. For this we introduce a natural hierarchy of pr...
We propose a new learning algorithm for the set covering machine and a tight data-compression risk bound that the learner can use for choosing the appropriate tradeoff between the ...
Abstract. In this paper we initiate an exploration of relationships between “preference elicitation”, a learning-style problem that arises in combinatorial auctions, and the pr...
Avrim Blum, Jeffrey C. Jackson, Tuomas Sandholm, M...
We introduce a computationally feasible, "constructive" active learning method for binary classification. The learning algorithm is initially formulated for separable cl...
We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a pri...