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Multi-layer perceptrons and trained classification trees are two very different techniaues which have recentlv become popuiar. Given enough data and time, both methods are capable of performing arbitrary non-linear classification. These two techniques, which developed out of different research communities, have not been previously compared on real-world problems. We first consider the important differences between multi-layer perceptrons and classification trees and conclude that there is notdoi:10.1109/icsmc.1989.71429 dblp:conf/smc/AtlasCPEMLCM89 fatcat:gyy2ug3qangu7iz7mjgxufv4rm