We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equival...
In this work, we propose a coordinator control recipe in the context of a batch process with the use of elements of petri nets and some techniques associated with non linear contr...
Jose Francisco, Briones de la Torre, Antonio Espu&...
We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a "null category noise model" (NCN...
We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the optimal bandwidth of a map is input dependent. The MGP is derived from the...
Abstract. Business-driven development favors the construction of process modifferent abstraction levels and by different people. As a consequence, there is a demand for consolidati...