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 normalized 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 number of rules and the number of conjuncts. After training, the re

• Type: Conference
• Year: 1992
• Where: NIPS
• Authors: Volker Tresp, Jürgen Hollatz, Subutai Ahmad
• Keywords: Certain Forms, NIPS 1992, NIPS 2007, Normalized Basis Functions, Rule-based Knowledge