Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
— A paramount challenge in probabilistic databases is the scalable computation of confidences of tuples in query results. This paper introduces an efficient secondary-storage o...
Interest in XML databases has been growing over the last few years. In this paper, we study the problem of incorporating probabilistic information into XML databases. We propose th...
Abstract. We present a probabilistic algorithm for finding correspondences across multiple images. The algorithm runs in a distributed setting, where each camera is attached to a s...
Abstract. We introduce a mathematical framework for black-box software testing of functional correctness, based on concepts from stochastic process theory. This framework supports ...