The Problem of Organizing and Partitioning Large Data Sets in Learning Algorithms for SOM-RBF Mixed Structures - Application to the Approximation of Environmental Variables

Abstract

The paper presents a technique to partition and sort data in a large training set for building models of environmental function approximation using RBFs networks. This process allows us to make very accurate approximations of the functions in a time fraction related to the RBF networks classic training proccess. Furthermore, this technique avoids problems of buffer overflow in the training algorithm execution. The results obtained proved similar accuracy to those obtained with a classical model in a time substantially less, opening, on the other hand, the way to the parallelization process using GPUs technology.