Maximum certainty approach to feedforward neuralnetworks

13 February 1997

journal article
Published by Institution of Engineering and Technology (IET) in Electronics Letters

Vol. 33 (4) , 306-307
https://doi.org/10.1049/el:19970211

Abstract

A Bayesian-based methodology is presented which leads to a data analysis system based around a committee of radial-basis function (RBF) networks. The authors show that this approach enables estimatation of the uncertainty associated with system outputs. Systems with differing numbers of internal degrees of freedom (weights) may hence be compared using training data only.

Keywords

DATA ANALYSIS SYSTEM
RBF NETWORKS
WEIGHTS
BAYESIAN-BASED METHODOLOGY
SYSTEM OUTPUT UNCERTAINTY
UNCERTAINTY ESTIMATION
FEEDFORWARD NEURAL NETWORKS
RADIAL-BASIS FUNCTION NETWORKS
TRAINING DATA
MAXIMUM CERTAINTY APPROACH

This publication has 3 references indexed in Scilit:

Pattern Recognition and Neural Networks
Published by Cambridge University Press (CUP) ,1996
Neural Networks
Published by Taylor & Francis ,1996
Neural Network Classifiers Estimate Bayesian a posteriori Probabilities
Neural Computation, 1991