This paper addresses the inference of probabilistic classification models using weakly supervised learning. In contrast to previous work, the use of proportion-based training data...
Carla Scalarin, Jacques Masse, Jean-Marc Boucher, ...
Real-world data -- especially when generated by distributed measurement infrastructures such as sensor networks -- tends to be incomplete, imprecise, and erroneous, making it impo...
We introduce a model class for statistical learning which is based on mixtures of propositional rules. In our mixture model, the weight of a rule is not uniform over the entire ins...
While there is a large class
of Multiple-Target Tracking (MTT) problems for which batch
processing is possible and desirable, batch MTT remains relatively
unexplored in comparis...
We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (P...