Abstract. We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We...
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing a previously-bui...
Benjamin Stewart, Jonathan Ko, Dieter Fox, Kurt Ko...
We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian mod...
The Hierarchical Mixture of Experts (HME) is a well-known tree-structured model for regression and classification, based on soft probabilistic splits of the input space. In its o...