Two Neural Network Construction Methods
Neural Processing Letters
Recursive Partitioning Technique for Combining Multiple Classifiers
Neural Processing Letters
Classifier Instability and Partitioning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Comparison of Methods for Learning of Highly Non-separable Problems
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
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A new technique called sequential window learning (SWL), for the construction of two-layer perceptrons with binary inputs is presented. It generates the number of hidden neurons together with the correct values for the weights, starting from any binary training set. The introduction of a new type of neuron, having a window-shaped activation function, considerably increases the convergence speed and the compactness of resulting networks. Furthermore, a preprocessing technique, called hamming clustering (HC), is proposed for improving the generalization ability of constructive algorithms for binary feedforward neural networks. Its insertion in the sequential window learning is straightforward. Tests on classical benchmarks show the good performances of the proposed techniques, both in terms of network complexity and recognition accuracy