This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Background: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interact...
Abstract. In supervised learning, discretization of the continuous explanatory attributes enhances the accuracy of decision tree induction algorithms and naive Bayes classifier. M...
We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian) network to represent features of the domain that are needed to identify users’ plans and...
David W. Albrecht, Ingrid Zukerman, Ann E. Nichols...
We propose a novel method for identifying road vehicles between two non-overlapping cameras. The problem is formulated as a same-different classification problem: probability of t...