Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to estimate an informative prior for a black-box model (e.g. a multilayer perceptron). Two technical approaches are proposed for the transformation of...

In this paper, we will consider the problem of classifying electroencephalogram (EEG) signals of normal subjects, and subjects suuering from psychiatric disorder, e.g., obsessive compulsive disorder, schizophrenia, using a class of artiicial neural networks, viz., multi-layer perceptron. It is shown that the multilayer perceptron is capable of classifying unseen test EEG signals to a high degre...

Nonlinear transformation is one of the major obstacles to analyzing the properties of multilayer perceptrons. In this letter, we prove that the correlation coefficient between two jointly Gaussian random variables decreases when each of them is transformed under continuous nonlinear transformations, which can be approximated by piecewise linear functions. When the inputs or the weights of a mul...

This paper describes the implementation of a Multilayer Perceptron Neural Network for handwritten digit recognition. The paper provides the knowledge about previously implemented techniques for same application and also provides their merits and demerits. In this paper Optimal Multilayer Perceptron.Neural network has been designed to reduce complexity of the circuit. Results are also stated to ...

Most current technologies in modeling time varying processes aim to adapt a static model over time in what has become to be known as continuous learning. We propose here a different approach to the same problem domain that of including the time explicitly in the modeling. An example implementation of this strategy is given in form of a multilayer perceptron with explicit time input. The perform...

Multilayer Perceptrons and trained classification trees are two very different techniques which have recently become popular. Given enough data and time, both methods are capable of performing arbitrary nonlinear classification. We first consider the important differences between multilayer Perceptrons and classification trees and conclude that there is not enough theoretical basis for the clea...