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...
— This paper presents an mathematical model that establishes the relation between the relevant cutting process parameters and surface quality, cutting forces and rate removal, in...
Abstract. Authors extend the multi-parameter attacktree model to include inaccurate or estimated parameter values, which are modelled as probabilistic interval estimations. The pap...