Fuzzily modular single-layer RBF neural networks for solving large-scale classification problems

This paper presents a type of combinative and modular single-layer radial basis function (RBF) neural network classifiers for solving the large-scale learning problems. We pay attention simultaneously to large samples, high dimensionality and multiple categories, not to only one or two terms among them. Above all, we divide a large-scale learning problem into multiple limited-scale simple problems. Each learning subset only includes a small part of samples from the original learning set. And furthermore, we propose a kind of modular single-layer RBF classifiers, in which each module is made up of multiple RBF kernels. The optimization method for determining the number, locations, widths and target values of RBF kernels is gone into details. The real output of one module is the sum of its components, and the class label of a certain sample is finally determined by the module with the maximum output. An application example, i.e., letter recognition, shows that the proposed RBF network is quite effective for solving the large-scale learning problems