Construction of neural networks on structured domains

We propose an entropy-based approach for automatically constructing neural networks consisting of generalized recursive neurons for classification of structured patterns. Given a classification problem, the architecture, i.e., the number of hidden layers and the number of nodes in each hidden layer, and all the values of the weights associates with the corresponding neural network are determined. As a result, the burden of trial-and-error imposed on the user can be avoided.