Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
This paper introduces an approach for closer integration of selforganizing maps into the visualization of spatio-temporal phenomena in GIS. It is proposed to provide a more explic...
In this paper we introduce a novel technique for modeling and recognizing gesture signals in 2D space. This technique is based on measuring the direction of the gradient of the mo...
Abstract-- In this paper, we address the issue of forecasting for periodically measured nonstationary traffic based on statistical time series modeling. Often with time series base...
Different credit assignment strategies are investigated in a two level co-evolutionary model which involves a population of Gaussian neurons and a population of radial basis funct...