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Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Computers
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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ACM SIGKDD Explorations Newsletter
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An adaptive high-order neural tree for pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Neural Networks
A self-organizing neural tree for large-set pattern classification
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
Fast training algorithms for multilayer neural nets
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Classification trees with neural network feature extraction
IEEE Transactions on Neural Networks
Cline: A New Decision-Tree Family
IEEE Transactions on Neural Networks
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In this paper, a new hybrid classifier is proposed by combining neural network and direct fractional-linear discriminant analysis (DF-LDA). The proposed hybrid classifier, neural tree with linear discriminant analysis called NTLD, adopts a tree structure containing either a simple perceptron or a linear discriminant at each node. The weakly performing perceptron nodes are replaced with DF-LDA in an automatic way. Taking the advantage of this node substitution, the tree building process converges faster and avoids the over-fitting of complex training sets in training process resulting a shallower tree together with better classification performance. The proposed NTLD algorithm is tested on various synthetic and real datasets. The experimental results show that the proposed NTLD leads to very satisfactory results in terms of tree depth reduction as well as classification accuracy.