Abstract
Statistical mechanics is applied to study the generalization properties of a two-layer neural network trained to implement a linearly separable problem. For a stochastic learning algorithm the generalization error as a function of the training set size is calculated exactly. The network with three hidden units experiences two first-order phase transitions due to an asymmetric freezing of the hidden units. Compared to a simple perceptron the committee machine is found to generalize worse
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