On the generalization error of fixed combinations of classifiers ## AbstractWe consider the generalization error of concept learning when using a fixed Boolean function of the outputs of a number of different classifiers. Here, we take into account the `margins' of each of the constituent classifiers. A special case is that in which the constituent classifiers are linear threshold functions (or perceptrons) and the fixed Boolean function is the majority function. This corresponds to a `committee of perceptrons', an artificial neural network (or circuit) consisting of a single layer of perceptrons (or linear threshold units) in which the output of the network is defined to be the majority output of the perceptrons. Recent work of Auer {\em et al.} studied the computational properties of such networks (where they were called `parallel perceptrons'), proposed an incremental learning algorithm for them, and demonstrated empirically that the learning rule is effective. As a corollary of the results presented here, generalization error bounds are derived for this special case that provide further motivation for the use of this learning rule.
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