Prototype-based kernels for extreme learning machines and radial basis function networks

Abstract

Extreme learning machines or radial basis function networks depends on kernel functions. If the kernel set is too small or not adequate (for the problem/learning data) the learning can be fruitless and generalization capabilities of classifiers do not become rewarding. The article presents a method of automatic stochastic selection of kernels. Thanks to the proposed scheme of kernel function selection we obtain the proper number of kernels and proper placements of kernels. Evaluation results clearly show that this methodology works very well and is superior to standard extreme learning machine, support vector machine or k nearest neighbours method.