title: Confidence in prediction by neural networks.
abstract: The idea that a trained network can assign a confidence number to its prediction, indicating the level of its reliability, is addressed and exemplified by an analytical examination of a perceptron with discrete and continuous output units. Results are derived for both Gibbs and Bayes scenarios. The information gain by the confidence number is estimated by various entropy measurements.
authors.affiliation: Minerva Center and Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel.
authors.forename: L
authors.initials: L
authors.lastname: Ein-Dor
authors.forename: I
authors.initials: I
authors.lastname: Kanter
mesh_terms: D019277:Entropy; D006801:Humans; D007858:Learning; D015233:Models, Statistical; D009415:Nerve Net