A Hybrid Connectionist, Symbolic Learning System

Allen Lawrence O. Hall, Steve G. Romaniuk

This paper describes the learning part of a system which has been developed to provide expert systems capability augmented with learning. The learning scheme is a hybrid connectionist, symbolic one. A network representation is used. Learning may be done incrementally and requires only one pass through the data set to be learned. Attribute, value pairs are supported as a variable implementation. Variables are represented by groups of connected cells in the network. The learning algorithm is described and an example given. Current results are discussed, which include learning the well-known Iris data set. The results show that the system has promise.


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