Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes...
Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, sc...
Leonardo Chang, Miriam Monica Duarte, Luis Enrique...
We propose a new approach for learning Bayesian classifiers from data. Although it relies on traditional Bayesian network (BN) learning algorithms, the effectiveness of our approa...
This work proposes the exploration of student’s information through the use of Bayesian Networks. By using thisapproach we aim to model the uncertainty inherent to the student...
The NLP systems often have low performances because they rely on unreliable and heterogeneous knowledge. We show on the task of non-anaphoric it identification how to overcome the...