Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
Abstract This paper investigates whether a machine can automatically learn the task of finding, within a large collection of candidate responses, the answers to questions. The lea...
Adam L. Berger, Rich Caruana, David Cohn, Dayne Fr...
Abstract. Detecting repeated portions of strings has important applications to many areas of study including data compression and computational biology. This paper defines and pres...
This paper describes an approach for application specific conflict prevention based on model-driven refinement of policies prior to deployment. Central to the approach is an algori...
Abstract. This paper concerns the iterative implementation of a knowledge model in a data mining context. Our approach relies on coupling a Bayesian network design with an associat...