Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
The purpose of this work is to propose an immune-inspired setup to use a self-organizing map as a computational model for the interaction of antigens and antibodies. The proposed ...
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
This paper proposes MISSA, a novel middleware to facilitate the development and provision of stream-based services in emerging pervasive environments. The streambased services util...
Decision algorithms are developed that use periods of intracranial non-seizure (interictal) EEG to localize epileptogenic networks. Depth and surface recordings are considered fro...
Justin Dauwels, Emad N. Eskandar, Andy Cole, Dan H...