Arbeitspapiere der GMD 735

Abstract

The problem of selecting critical examples for training neural networks is discussed. Based on Bayesian statistics and information theory we derive a measure of criticality and present a computationally efficient method for selecting critical examples. This leads to a new type of learning scheme in which the network is trained incrementally on an increasing… (More)

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