Knowledge-based connectionism for revising domain theories

A knowledge-based connectionist model for machine learning referred to as KBCNN is presented. In the KBCNN learning model, useful domain attributes and concepts are first identified and linked in a way consistent with initial domain knowledge, and then the links are weighted properly so as to maintain the semantics. Hidden units and additional connections may be introduced into this initial connectionist structure as appropriate. Then, this primitive structure evolves to minimize empirical error. The KBCNN learning model allows the theory learned or revised to be translated into the symbolic rule-based language that describes the initial theory. Thus, a domain theory can be pushed onto the network, revised empirically over time, and decoded in symbolic form. The domain of molecular genetics is used to demonstrate the validity of the KBCNN learning model and its superiority over related learning methods