In this paper, denoising on multicomponent images is performed. The presented procedure is a spatial waveletbased denoising techniques, based on Bayesian leastsquares optimization...
Cognitive architectures need to resolve the diversity dilemma – i.e., to blend diversity and simplicity – in order to couple functionality and efficiency with integrability, e...
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irreleva...
— This paper describes an ontology-driven model, which integrates Bayesian Networks (BN) into the Ontology Web Language (OWL) to preserve the advantages of both. This model makes...
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a...