Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" lab...
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Type classes have found a wide variety of uses in Haskell programs, from simple overloading of operators (such as equality or ordering) to complex invariants used to implement typ...
We study the complexity issues for Walrasian equilibrium in a special case of combinatorial auction, called single-minded auction, in which every participant is interested in only ...
A variety of information extraction techniques rely on the fact that instances of the same relation are "distributionally similar," in that they tend to appear in simila...