A Two-Phase RBF-ELM Learning Algorithm

Abstract

A variant of extreme learning machine (ELM) named RBF-ELM was proposed by Huang et al. in 2004. The RBF-ELM is tailored for radial basis function (RBF) networks. Similar to ELM, RBF-ELM also employs randomized method to initialize the centers and widths of RBF kernels, and analytically calculate the output weights of RBF networks. In this paper, we proposed a two-phase RBF-ELM learning algorithm, which only randomly initializes the width parameters. The center parameters are determined by an instance selection method. The first phase of the proposed algorithm is to select the centers of the RBF network rather than randomly initializing. The second phase is to train the RBF network with ELM. Compared with the RBF-ELM, the experimental results show that the proposed algorithm can improve the testing accuracy.KeywordsExtreme learning machineRadial basis functionInstance selectionData condensationMulti-scale