An approach to improve online sequential extreme learning machines using restricted Boltzmann machines

Abstract

The extreme learning machine (ELM) is an analytical algorithm developed to train single-hidden layer feedforward neural network (SLFN). Although the ELM provides a good generalization and a very fast training phase, two shortcomings can be pointed in this approach: it assigns its input weights randomly and it is not able to handle sequential learning. Thereby, the online sequential extreme learning machine (OS-ELM) was proposed in order to tackle the lack of sequential learning on ELMs. Nonetheless, the OS-ELM still assigns the input weights randomly. Thus, in this work, we proposed an approach based on a restricted Boltzmann machine (RBM) to improve the OS-ELM by determining its input weights throughout the training phase. We applied our method to several well-known benchmarks and the results show that the proposed approach is able to improve the OS-ELM for most them.