A novel constructive neural network that learns to find discriminant functions

This paper presents a novel architecture based on a constructive algorithm that allows the network to grow attending to both supervised and unsupervised criteria. The main goal is to end up with a set of discriminant functions able to solve a multi-class classification problem. The main difference with well-known NN-classificators lean on the fact that training is performed over labeled sets of patterns that we call high-level-structures (HLS). Every set contain patterns linked each other by some physical evidence, like neighbor pixels in a subimage or a time-sequence of frequency vectors in a speech utterance, but the membership of every individual pattern in the high-level-structure can not be so clear. This architecture has been tested on a number of artificial data sets and real data sets with very good results. We are now applying the algorithm to classification of real images drawn from the DataBase created for the ALINSPEC project.