A self organizing ARTMAP neural architecture for supervised learning and pattern recognition

Author(s): Carpenter, G.A. | Grossberg, S. | Reynolds, J.H. |

Year: 1991

Citation: In T. Kohonen, K. M?kasira, O. Simula, & J. Kangas (Eds.), Artificial Neural Networks, Amsterdam: North Holland/Elsevier Science Publishing, I 31-36.

Abstract: This paper announces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors in recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training, the ART-A modules receives a stream of {Ap} input patterns, and ART-B receives a stream of {Bp} input patterns, where Bp is the correct prediction given Ap. These ART modules are linked by an associative learning network and an internal controller that ensure autonomous system operation in real time. During test trials, the remaining patterns Ap are presented without Bp, and their predictions at ART-B are compared with Bp.

Topics: Machine Learning, Models: ARTMAP,

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