A self-organizing neural tree for large-set pattern classification

Neural networks have been successfully applied to various pattern classification problems in terms of their learning ability, high discrimination power, and excellent generalization ability. However, for the case of classifying large-set and complex patterns, the greater part of conventional neural networks suffer from several difficulties such as the determination of the structure and size of the network, the computational complexity, and so on. In this paper, to cope with these difficulties, we propose a structurally adaptive intelligent neural tree (SAINT). The basic idea is to partition hierarchically input pattern space using a tree-structured network which is composed of subnetworks with topology-preserving mapping ability. The main advantage of SAINT is that it attempts to find automatically a network structure and size suitable for the classification of large-set and complex patterns through structure adaptation. Experimental results reveal that SAINT is very effective for the classification of large-set real world handwritten characters with high variations, as well as multilingual, multifont, and multisize large-set characters.