Training Neural Networks with Deficient Data
Volker Tresp, Subutai Ahmad, Ralph Neuneier · 1993
We analyze how data with uncertain or missing input features can be incorporated into the training of a neural network. The general solution requires a weighted integration over the unknown or uncertain input although computationally cheaper closed-form solutions can be found for certain Gaussian Basis Function (GBF) networks. We also discuss cases in which heuristical solutions such as substituting the mean of an unknown input can be harmful. 1 INTRODUCTION The ability to learn from data with uncertain and missing information is a fundamental requirement for learning systems. In the "real world", features are missing due to unrecorded information or due to occlusion in vision, and measurements are affected by noise. In some cases the experimenter might want to assign varying degrees of reliability to the data. In regression, uncertainty is typically attributed to the dependent variable which is assumed to be disturbed by additive noise. But there is no reason to assume that input f...