Network Structuring and Training Using Rule-based Knowledge

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.

Authors

• Volker Tresp Ludwig Maximilian University of Munich, Munich, Germany Munich Center for Machine Learning, Munich, Germany
• Jürgen Hollatz
• Subutai Ahmad

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