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 real-world and synthetic data, using different learning strategies, training set sizes, and hyper-parameter values, the generalization error and resources requirements of this neural network(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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