Learning by a population of perceptrons

We study learning from example~ by a population of neural networks. A group of single layer perceptions with discrete weights learn from a two-layer neural network. Each member is trained independently from the same or independent example sets. They vote for an answer of new problems. We calculate the generalization performance of the group decision by majority vote. The generalization error decreases to a minimum at a certain number of examples and increases again.