We consider a setting for discriminative semisupervised learning where unlabeled data are used with a generative model to learn effective feature representations for discriminativ...
— Learning motion models of a moving object is a challenge for autonomous robots. We address the particular instance of parameter learning when tracking object motions in a switc...
Abstract. Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the deci...
Cognitive trait model (CTM) is a student model that aims to create profiles of learners’ cognitive traits. Divergent associative learning (DAL) denotes the characteristic of lea...
In this paper we propose a framework for decentralized model-based diagnosis of complex systems modeled with qualitative constraints and whose models are distributed among their s...