Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models fo...
Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
Abstract. The purpose of information extraction (IE) is to find desired pieces of information in natural language texts and store them in a form that is suitable for automatic pro...
Modularization and abstraction are the keys to practical verification and analysis of large and complex systems. We present in an incremental methodology for the automatic analysi...
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical too...