A mapping from linear tree classifiers to neural net classifiers

Both neural net classifiers utilizing multilayer perceptron and linear tree classifiers composed of hierarchically structured linear discriminant functions can form arbitrarily complex decision boundaries in the feature space and have similar decision making process. The structure of the linear tree classifier can be easily mapped to that of the neural nets having two hidden layers by using the hyperplanes produced by the linear tree classifier. A new method for mapping the linear tree classifier to the neural nets having one hidden layer is presented with theoretical basis of mapping the convex decision regions produced by the linear tree classifier to the neurons in the neural nets. This mapping has been shown to be useful for choosing appropriately sized neural nets having one or two hidden layers.<
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