MicroARTMAP for pattern recognition problems
Advances in Engineering Software
GFAM: Evolving Fuzzy ARTMAP neural networks
Neural Networks
AG-ART: An adaptive approach to evolving ART architectures
Neurocomputing
An adaptive multiobjective approach to evolving ART architectures
IEEE Transactions on Neural Networks
Semi-supervised Bayesian ARTMAP
Applied Intelligence
Hierarchical polytope ARTMAP for supervised learning
Journal of Computer Science and Technology
Incremental rule pruning for fuzzy ARTMAP neural network
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Multilayer Fuzzy ARTMAP: fast learning and fast testing for pattern classification
Proceedings of the 27th Annual ACM Symposium on Applied Computing
GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm
Artificial Intelligence Review
Bayesian ARTMAP for regression
Neural Networks
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A new architecture called μARTMAP is proposed to impact a category proliferation problem present in Fuzzy ARTMAP. Under a probabilistic setting, it seeks a partition of the input space that optimizes the mutual information with the output space, but allowing some training error, thus avoiding overfitting. It implements an inter-ART reset mechanism that permits handling exceptions correctly, thus using few categories, especially in high dimensionality problems. It compares favorably to Fuzzy ARTMAP and Boosted ARTMAP in several synthetic benchmarks, being more robust to noise than Fuzzy ARTMAP and degrading less as dimensionality increases. Evaluated on a real-world task, the recognition of handwritten characters, it performs comparably to Fuzzy ARTMAP, while generating a much more compact rule set