SUPERVISED NEURAL NETWORKS FOR THE CLASSIFICATION OF STRUCTURES

Until now neural networks have been used for classifying unstructured patterns and sequences, However, standard neural networks and statisti cal methods are usually believed to be inadequate when dealing with co mplex structures because of their feature-based approach, In fact, fea ture-based approaches usually fail to give satisfactory solutions beca use of the sensitivity of the approach to the a priori selection of th e features, and the incapacity to represent any specific information o n the relationships among the components of the structures, However, w e show that neural networks can, in fact, represent and classify struc tured patterns, The key idea underpinning our approach is the use of t he so called ''generalized recursive neuron,'' which is essentially a generalization to structures of a recurrent neuron, By using generaliz ed recursive neurons, all the supervised networks developed for the cl assification of sequences, such as backpropagation through time networ ks, realtime recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees can, on the whole, be g eneralized to structures. The results obtained by some of the above ne tworks (with generalized recursive neurons) on the classification of l ogic terms are presented.