Hierarchical Growing Cell Structures: TreeGCS
IEEE Transactions on Knowledge and Data Engineering
Analysis and Recognition of Asian Scripts - the State of the Art
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A method of face recognition based on fuzzy clustering and parallel neural networks
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Parallel-series perceptrons for the simultaneous determination of odor classes and concentrations
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Expert Systems with Applications: An International Journal
Incorporating linear discriminant analysis in neural tree for multidimensional splitting
Applied Soft Computing
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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, etc. To cope with these difficulties, we propose a structurally adaptive intelligent neural tree (SAINT). The basic idea is to partition hierarchically the 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