A tree structured neural network
Hazem M. Raafat, Mohsen Rashwan · 2002
A tree structured system for pattern classification is proposed. It uses the feedforward neural network with back-propagation (FN) as a building block. A single FN is used to classify all of the given patterns, then a confusion matrix is carefully studied and used to divide the patterns into groups. This process is repeated by training new FNs with these groups then dividing them into subgroups and so on, until no more grouping could be obtained. It is shown that by this approach, the available feature set can be used more effectively. The testing environment of this work is the isolated handwritten Arabic character set, which is a problem of reasonable complexity. However, the suggested method can be applied to other pattern classification problems. Dividing a large problem into smaller and easier ones is the target that is successful reached.>