Knowledge extraction from radial basis function networks and multilayer perceptrons
نویسندگان
چکیده
Recently there has been a lot of interest in the extrac tion of symbolic rules from neural networks The work described in this paper is concerned with an evaluation and comparison of the accuracy and complexity of sym bolic rules extracted from radial basis function networks and multi layer perceptrons Here we examine the abil ity of rule extraction algorithms to extract meaningful rules that describe the overall performance of a particu lar network In addition the research also highlights the suitability of a speci c neural network architecture for particular classi cation problems The research carried out on the extracted rule quality and complexity also has a direct bearing on the use of rule extraction algorithms for data mining and knowledge discovery
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