This paper was prepared for a lecture at a recent meeting of the German Chapter of ISKO devoted to Wissensorganisation mit Multimedialen Techniken [Knowledge Organization with Mul...
This research introduces a general class of functions serving as generalized neuron models to be used in artificial neural networks. They are cast in the common framework of comp...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
Models are increasingly being relied upon to inform and support natural resource management. They are incorporating an ever broader range of disciplines and now often confront peo...
Anthony J. Jakeman, Rebecca A. Letcher, John P. No...