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...
This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...
Probabilistic models of languages are fundamental to understand and learn the profile of the subjacent code in order to estimate its entropy, enabling the verification and predicti...
A widely used computational model for constructing fault-tolerant distributed applications employs atomic transactions for controlling operations on persistent objects. There has ...
Transferring knowledge from one domain to another is challenging due to a number of reasons. Since both conditional and marginal distribution of the training data and test data ar...