Generating Predicate Rules from Neural Networks

Artificial neural networks play an important role for pattern recognition tasks. However, due to poor comprehensibility of the learned network, and the inability to represent explanation structures, they are not considered sufficient for the general representation of knowledge. This paper details a methodology that represents the knowledge of a trained network in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner.

• Type: Conference
• Year: 2005
• Where: IDEAL
• Authors: Richi Nayak
• Keywords: Artificial Neural Networks, IDEAL 2005, Knowledge Based Reasoner, Pattern Recognition Tasks