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Author(s): Dimitriadis, Y.A. | GomezSanchez, E. | CanoIzquierdo, J.M. | LopezCoronado, J. |
Year: 2002
Citation: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 1.
Abstract: A new architecture, called ARTMAP, 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.
Topics:
Machine Learning,
Models:
Fuzzy ARTMAP,
Modified ART,
