Committee machines for facial-gender recognition
International Journal of Hybrid Intelligent Systems
Neural Networks
A neural tree and its application to spam e-mail detection
Expert Systems with Applications: An International Journal
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
Analyzing large image databases with the evolving tree
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Novel initialization scheme for Fuzzy C-Means algorithm on color image segmentation
Applied Soft Computing
Color image segmentation using adaptive unsupervised clustering approach
Applied Soft Computing
International Journal of Computer Vision and Image Processing
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Presents competitive neural trees (CNeTs) for pattern classification. The CNeT contains m-ary nodes and grows during learning by using inheritance to initialize new nodes. At the node level, the CNeT employs unsupervised competitive learning. The CNeT performs hierarchical clustering of the feature vectors presented to it as examples, while its growth is controlled by forward pruning. Because of the tree structure, the prototype in the CNeT close to any example can be determined by searching only a fraction of the tree. The paper introduces different search methods for the CNeT, which are utilized for training as well as for recall. The CNeT is evaluated and compared with existing classifiers on a variety of pattern classification problems