We explore the use of information models as a guide for the development of single objective optimization algorithms, giving particular attention to the use of Bayesian models in a...
Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a...
Allowing the student to have some control over the diagnosis inspecting and changing the model the system has made of him is a feasible approach in student modelling which tracks t...
Abstract. This paper studies a statistical skin-color model and its adaptation. It is revealed that 1 human skin colors cluster in a small region in a color space; 2 the variance o...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...