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An associative hierarchical self-organizing system

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1 Author(s)
Barry R. Davis ; University of Texas School of Public Health, Houston, TX 77030, USA

A system that learns to predict events in various environments is described. The system is associative and distributed; a hierarchical self-organization of low-level units into high-level units takes place based on experience in a particular domain. Its design is inspired by widely held principles of brain organization and by some newly developed techniques in nonparametric statistical inference. The system can be regarded as a realization of a nonparametric statistical algorithm. This is demonstrated by a discussion of system architecture and a presentation of an application in a `number theory' environment.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics  (Volume:SMC-15 ,  Issue: 4 )