Single-layer learning revisited: a stepwise procedure for building and training a neural network

Abstract

A stepwise procedure for building and training a neural network intended to perform classification tasks, based on single layer learning rules, is presented. This procedure breaks up the classification task into subtasks of increasing complexity in order to make learning easier. The network structure is not fixed in advance: it is subject to a growth process during learning. Therefore, after training, the architecture of the network is guaranteed to be well adapted for the classification problem.