Philippe Henniges

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In this paper, the impact on fuzzy ARTMAP performance of decisions taken for batch supervised learning is assessed through computer simulation. By learning different realworld and synthetic data, using different learning strategies, training set sizes, and hyperparameter values, the generalization error and resources requirements of this neural network are(More)
In this paper, the impact of overtraining on the performance of fuzzy ARTMAP neural networks is assessed for pattern recognition problems consisting of overlapping class distributions, and consisting of complex decision boundaries with no overlap. Computer simulations are performed with fuzzy ARTMAP networks trained for one epoch, through cross-validation,(More)
In this paper a particle swarm optimization (PSO)-based training strategy is introduced for fuzzy ARTMAP that minimizes generalization error while optimizing parameter values. Through a comprehensive set simulations, it has been shown that this training strategy allows fuzzy ARTMAP to achieve a significantly lower generalization error than when it uses(More)
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