Bounds on the Complexity of Testing and Loading Neurons

In this paper we investigate two distinct aspects of the learning theory of the boolean linear threshold neuron and the sigmoid neuron. First, we present results on the number of examples needed for testing a given boolean linear threshold function. Then, using this theory, we analyse the time some well-known learning algorithms require to load a given training sample onto a simple boolean perceptron and a single sigmoid neuron.