This paper addresses the supervised learning in which the class membership of training data are subject to uncertainty. This problem is tackled in the framework of the Dempster-Sha...
Abstract. Automatic pattern classifiers that allow for on-line incremental learning can adapt internal class models efficiently in response to new information without retraining fr...
: This paper describes a rather novel method for the supervised training of regression systems that can be an alternative to feedforward Artificial Neural Networks (ANNs) trained w...
Mark J. Embrechts, Dirk Devogelaere, Marcel Rijcka...
In the neural network literature, input feature de-correlation is often referred as one pre-processing technique used to improve the MLP training speed. However, in this paper, we...
Barycentric plotting, achieved by placing gaussian kernels in distant corners of the feature space and projecting multidimensional output of neural network on a plane, provides inf...