Abstract
The use of small multilayer nets at the decision nodes of a binary
classification tree to extract nonlinear features is proposed. This
approach exploits the power of tree classifiers to use appropriate local
features at the different levels and nodes of the tree. The nets are
trained and the tree is grown using a gradient-type learning algorithm
in conjunction with a heuristic class aggregation algorithm. The method
improves on standard classification tree design methods in that it
generally produces trees with lower error rates and fewer nodes. It also
provides a structured approach to neural network classifier design which
reduces the problem associated with training large unstructured nets,
and transfers the problem of selecting the size of the net to the
simpler problem of finding the right size tree. Example concern waveform
and handwritten character recognition
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