Abstract. We develop a probabilistic interpretation of non-linear component extraction in neural networks that activate their hidden units according to a softmaxlike mechanism. On ...
In solving application problems, the data sets used to train a neural network may not be hundred percent precise but within certain ranges. Representing data sets with intervals, ...
Abstract. We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete a...
This paper describes an evaluation of a neural network technique for modelling fuel spray penetration in the cylinder of a diesel internal combustion engine. The model was implemen...
Simon D. Walters, Shaun H. Lee, Cyril Crua, Robert...
Online adaptation is a key requirement for image processing applications when used in dynamic environments. In contrast to batch learning, where retraining is required each time a...