Effective learning in multi-label classification (MLC) requires an ate level of abstraction for representing the relationship between each instance and multiple categories. Curren...
A graphical multiagent model (GMM) represents a joint distribution over the behavior of a set of agents. One source of knowledge aboutagents'behaviormaycomefromgametheoretic ...
Quang Duong, Michael P. Wellman, Satinder P. Singh
The stochastic discrimination (SD) theory considers learning as building models of uniform coverage over data distributions. Despite successful trials of the derived SD method in s...
Approximate policy iteration methods based on temporal differences are popular in practice, and have been tested extensively, dating to the early nineties, but the associated conve...
This study investigates variational image segmentation with an original data term, referred to as statistical overlap prior, which measures the conformity of overlap between the no...