This paper reports work to support dependability arguments about the future reliability of a product before there is direct empirical evidence. We develop a method for estimating ...
Distance measures like the Euclidean distance have been the most widely used to measure similarities between feature vectors in the content-based image retrieval (CBIR) systems. H...
Riadh Ksantini, Djemel Ziou, Bernard Colin, Fran&c...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
In this paper we develop a novel probabilistic model of computational trust that allows agents to exchange and combine reputation reports over heterogeneous, correlated multi-dime...
Steven Reece, Stephen Roberts, Alex Rogers, Nichol...
Schema learning is a way to discover probabilistic, constructivist, predictive action models (schemas) from experience. It includes methods for finding and using hidden state to m...