Learning capacity and sample complexity on expert networks.
A major development in knowledge-based neural networks is the integration of symbolic expert rule-based knowledge into neural networks, resulting in so-called rule-based neural (or connectionist) networks. An expert network here refers to a particular construct in which the uncertainty management model of symbolic expert systems is mapped into the activation function of the neural network. This paper addresses a yet-to-be-answered question: Why can expert networks generalize more effectively from a finite number of training instances than multilayered perceptrons? It formally shows that expert networks reduce generalization dimensionality and require relatively small sample sizes for correct generalization.
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Dept. of Comput. and Inf. Sci., Florida Univ., Gainesville, FL.