Small Number of Hidden Units for ELM with Two-Stage Linear Model
IEICE - Transactions on Information and Systems
A growing and pruning method for radial basis function networks
IEEE Transactions on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
Efficiently explaining decisions of probabilistic RBF classification networks
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Active learning with the probabilistic RBF classifier
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Quality of classification explanations with PRBF
Neurocomputing
Intelligent Data Analysis
On-line modeling via fuzzy support vector machines and neural networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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The probabilistic radial basis function (PRBF) network constitutes a probabilistic version of the RBF network for classification that extends the typical mixture model approach to classification by allowing the sharing of mixture components among all classes. The typical learning method of PRBF for a classification task employs the expectation-maximization (EM) algorithm and depends strongly on the initial parameter values. In this paper, we propose a technique for incremental training of the PRBF network for classification. The proposed algorithm starts with a single component and incrementally adds more components at appropriate positions in the data space. The addition of a new component is based on criteria for detecting a region in the data space that is crucial for the classification task. After the addition of all components, the algorithm splits every component of the network into subcomponents, each one corresponding to a different class. Experimental results using several well-known classification data sets indicate that the incremental method provides solutions of superior classification performance compared to the hierarchical PRBF training method. We also conducted comparative experiments with the support vector machines method and present the obtained results along with a qualitative comparison of the two approaches.