Network Structuring and Training Using Rule-based Knowledge

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Volker Tresp, Jürgen Hollatz, Subutai Ahmad


We demonstrate in this paper how certain forms of rule-based knowledge can be used to prestructure a neural network of nor(cid:173) malized basis functions and give a probabilistic interpretation of the network architecture. We describe several ways to assure that rule-based knowledge is preserved during training and present a method for complexity reduction that tries to minimize the num(cid:173) ber of rules and the number of conjuncts. After training the refined rules are extracted and analyzed.