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Author(s): Carpenter, G.A. | Ross, W.D. |
Year: 1994
Citation: Proceedings of the World Congress on Neural Networks (WCNN 94), I 749-758.
Abstract: ART-EMAP synthesizes adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). The network extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Simulations of the four ART-EMAP stages demonstrate performance on a difficult 3-D object recognition problem.
Topics:
Image Analysis,
Machine Learning,
Models:
Fuzzy ARTMAP,
Modified ART,
