Neural trees: a new tool for classification

The authors propose a new classifier based on neural network techniques. The ‘network’ consists of a set of perceptrons functionally organized in a binary tree (‘neural tree’). The learning algorithm is inspired from a growth algorithm, the tiling algorithm, recently introduced for feedforward neural networks. As in the former case, this is a constructive algorithm, for which convergence is guaranteed. In the neural tree one distinguishes the structural organization from the functional organization: each neuron of a neural tree receives inputs from, and only from, the input layer; its output does not feed into any other neuron, but is used to propagate down a decision tree. The main advantage of this approach is due to the local processing in restricted portions of input space, during both learning and classification. Moreover, only a small subset of neurons have to be updated during the classification stage. Finally, this approach is easily and efficiently extended to classification in a multiclass probl...

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