Machine learning using single-layered and multi-layered neural networks

Abstract

Methods are proposed which automatically extract a high level knowledge representation in the form of rules from the lower level representation used by neural networks. The strength of neural networks in dealing with noise has made it possible to produce correct rules in a noisy domain. Results obtained when applying the proposed method to a noisy domain suggest that this method can be used in real-world domains. It is believed that this work will lead to an area of machine learning which uses neural networks as the basis of knowledge acquisition which can deal with real-world difficulties.<>