Integrating symbolic and neural processing in a self-organizing architecture for pattern recognition and prediction

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

Year: 1994

Citation: In V. Honavar & L. Uhr (Eds.), Artificial Intelligence and Neural Networks: Steps Toward Principled Integration, San Diego, CA: Academic Press, 387-421.

Abstract: The apparent dichotomy between symbolic AI processing and distributed neural processing cannot be absolute, since neural networks that capture essential features of human intelligence will also model some of the symbolic processes of which humans are capable. Indeed, a primary goal of biological neural network research is to design systems that can self-organize intelligent symbolic processing capabilities. Such a system is summarized in this chapter...

Topics: Machine Learning, Models: ARTMAP, Fuzzy ARTMAP,

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