Confidence in prediction by neural networks.
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.
Ein-Dor
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Minerva Center and Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel.
Kanter
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D019277:Entropy; D006801:Humans; D007858:Learning; D015233:Models, Statistical; D009415:Nerve Net