Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
A Probabilistic RBF Network for Classification
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Shared kernel models for class conditional density estimation
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
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The Probabilistic RBF (PRBF) network constitutes a recently proposed classification network that employs Gaussian mixture models for class conditional density estimation. The particular characteristic of this model is that it allows the sharing of the Gaussian components of the mixture models among all classes, in the same spirit that the hidden units of a classification RBF network feed all output units. Training of the PRBF network is a likelihood maximization procedure based on the Expectation - Maximization (EM) algorithm. In this work, we propose a Bayesian regularization approach for training the PRBF network that takes into account the existence of ovelapping among classes in the region where a Gaussian component has been placed. We also propose a fast and iterative training procedure (based on the EM algorithm) to adjust the component parameters. Experimental results on well-known classification data sets indicate that the proposed method leads to superior generalization performance compared to the original PRBF network with the same number of kernels.