Abstract
In this paper we present a method for calculating , the generalization error of two-layered networks. is the fraction of the input space for which two networks yield different answers, therefore it is a good index to measure the similarity between them. The method presented here is an extension of work reported previously. It is applied here to the case of a single-layer perceptron (which can be regarded as a particular two-layered perceptron) that tries to imitate a two-layered network. The particular realizations of such a two-layered network that are analysed here are the `parity machine', the `and machine' and the `committee machine'. We have also compared the input - output mapping of a committee and a parity machine.
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