CNeT: competitive neural trees for pattern classification

This paper introduces competitive neural trees (CNeT) for pattern classification. The CNeT performs hierarchical classification and employs competitive unsupervised learning at the node level. The generalization ability of the CNeT is guaranteed by forward pruning, which is an inherent part of the learning process. Different search methods are introduced for the CNeT and used for both training and recall. The influence of different search methods on the performance of the CNeT is experimentally evaluated