Machine Vision and Applications
Hybrid high order neural networks
Applied Soft Computing
Exploiting temporal statistics for events analysis and understanding
Image and Vision Computing
Expert Systems with Applications: An International Journal
A Hybrid Higher Order Neural Classifier for handling classification problems
Expert Systems with Applications: An International Journal
Invariant set of weight of perceptron trained by perceptron training algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modular neural network programming with genetic optimization
Expert Systems with Applications: An International Journal
Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model)
Engineering Applications of Artificial Intelligence
A balanced neural tree for pattern classification
Neural Networks
Incorporating linear discriminant analysis in neural tree for multidimensional splitting
Applied Soft Computing
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A new neural tree model, called adaptive high-order neural tree (AHNT), is proposed for classifying large sets of multidimensional patterns. The AHNT is built by recursively dividing the training set into subsets and by assigning each subset to a different child node. Each node is composed of a high-order perceptron (HOP) whose order is automatically tuned taking into account the complexity of the pattern set reaching that node. First-order nodes divide the input space with hyperplanes, while HOPs divide the input space arbitrarily, but at the expense of increased complexity. Experimental results demonstrate that the AHNT generalizes better than trees with homogeneous nodes, produces small trees and avoids the use of complex comparative statistical tests and/or a priori selection of large parameter sets.