Philippe Henniges

• Master of Engineering
• École de Technologie Supérieure

Training fuzzy ARTMAP neural networks for classification using data from com-plex real-world environments may lead to category proliferation, and yield poor performance. This problem is known to occur whenever the training set contains noisy and overlapping data. Moreover, when the training set contains identical input patterns that belong to diffe...

Training fuzzy ARTMAP neural networks for classification using data from com- plex real-world environments may lead to category proliferation, and yield poor performance. This problem is known to occur whenever the training set contains noisy and overlapping data. Moreover, when the training set contains identical input patterns that belong to diff...

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 n...

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...

Applying fuzzy ARTMAP to complex real-world problems such as handwritten character recognition may lead to poor performance and a convergence problem whenever the training set contains very similar or identical patterns that belong to different classes. To circumvent this problem, some alternatives to the network's original match tracking (MT) proc...

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-val...