Distributed prediction and hierarchical knowledge discovery by ARTMAP neural networks

Author(s): Carpenter, G.A. |

Year: 2003

Citation: Proceedings of the 7th Annual International Conference of Knowledge-Based Intelligent Information and Engineering Systems (KES 03), Oxford, UK: Springer Berlin/Heidelberg, 1-4.

Abstract: Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction between top-down expectation and bottom-up input, thereby creating a focus of attention which, in turn, determines the nature of coded memories.

Topics: Machine Learning, Applications: Information Fusion, Models: ARTMAP, Modified ART,

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