MayBMS is a state-of-the-art probabilistic database management system which leverages the strengths of previous database research for achieving scalability. As a proof of concept ...
Jiewen Huang, Lyublena Antova, Christoph Koch, Dan...
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to de...
Automation of power system fault identification using information conveyed by the wavelet analysis of power system transients is proposed. Probabilistic Neural Network (PNN) for d...
This paper deals with the problem of error estimation in 3D reconstruction. It shows how interval analysis can be used in this way for 3D vision applications. The description of a...
Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an ...