On the effects of initialising a neural network with prior knowledge

Abstract

This paper quantitatively examines the effects of initialising a Rapid Backprop Network (REP) with prior domain knowledge expressed in the form of propositional rules. The paper first describes the RBP network and then introduces the RULEIN algorithm which encodes propositional rules as the weights of the nodes of the REP network. A selection of datasets is used to compare networks that began learning from tabula rasa with those that were initialised with varying amounts of domain knowledge prior to the commencement of the learning phase. Network performance is compared in terms of time to converge, accuracy at convergence, and network size at convergence.