Layered neural net design through decision trees

A multiple-layer artificial network (ANN) structure is capable of implementing arbitrary input-output mappings. Similarly, hierarchical classifiers, more commonly known as decision trees, possess the capabilities of generating arbitrarily complex decision boundaries in an n-dimensional space. Given a decision tree, it is possible to restructure it as a multilayered neural network. It is shown how this mapping of decision trees into a multilayer neural network structure can be exploited for the systematic design of a class of layered neural networks, called entropy nets, that have far fewer connections.<
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