Supervised learning of a parts-based model can be for-
mulated as an optimization problem with a large (exponen-
tial in the number of parts) set of constraints. We show how
thi...
M. Pawan Kumar, Andrew Zisserman, Philip H.S. Torr
Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. A predominant approach to collabo...
Practice has shown that providing content to students alone is not sufficient for good learning results. In computer based training (CBT) and especially webbased training (WBT) en...
Many AI problems can be modeled as constraint satisfaction problems (CSP), but many of them are actually dynamic: the set of constraints to consider evolves because of the environ...
There is increasing research interest in solving routing problems in sensor networks subject to constraints such as data correlation, link reliability and energy conservation. Sin...