Multilevel RBF to resolve classification problems with large training sets: new pseudo-exact procedure

This article describes a multilevel neural architecture based on SOMs and radial basis function networks, which can produce results and approximation of functions close to those obtained using a single radial basis function network. In addition, the learning algorithm of this new architecture delivers a very significant reduction in the training time of the network and, at the same time, allows parallelization techniques to be applied in a natural way. The proposed training system is not an iterative method , it is not exactly an exact one . Our proposal procedure requieres an amount of time similar to the iterative methods but with the efficiency of the exact methods . This architecture has been developed to test on problems involving ecological segmentation, as part of environmental study and monitoring programmes by research groups in southeastern Spain.