Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preprocessing of brain stem auditory evoked potentials for diagnosing multiple sclerosis
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
A novel BP neural network model for traffic prediction of next generation network
ICNC'09 Proceedings of the 5th international conference on Natural computation
Boosted Pre-loaded Mixture of Experts for low-resolution face recognition
International Journal of Hybrid Intelligent Systems
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An algorithmic procedure is developed for the random expansion of a given training set to combat overfitting and improve the generalization ability of backpropagation trained multilayer perceptrons (MLPs). The training set is K-means clustered and locally most entropic colored Gaussian joint input-output probability density function estimates are formed per cluster. The number of clusters is chosen such that the resulting overall colored Gaussian mixture exhibits minimum differential entropy upon global cross-validated shaping. Numerical studies on real data and synthetic data examples drawn from the literature illustrate and support these theoretical developments