Recent trends in ELM and MLELM: A review

Abstract

Extreme Learning Machine (ELM) is a high effective learning algorithm for the single hidden layer feed forward neural networks. Compared with the existing neural network learning algorithm it solves the slow training speed and over-fitting problems. It has been used in different fields and applications such as biomedical engineering, computer vision, remote sensing, chemical process and control and robotics. It has better generalization stability, sparsity, accuracy, robustness, optimal control and fast learning rate This paper introduces a brief review about ELM and MLELM, describing the principles and latest research progress about the algorithms, theories and applications. Next, Multilayer Extreme Learning Machine (MLELM) and other state-of-the-art classifiers are trained on this suitable training feature vector for classification of data. Deep learning has the advantage of approximating the complicated function and mitigating the optimization difficulty associated with deep models. Multilayer extreme learning machine is a learning algorithm of an Artificial Neural Network (ANN) which takes to be good for deep learning and extreme learning machine. This review presents a comprehensive view of these advances in ELM and MLELM which may be worthy of exploring in the future.