A review on extreme learning machine

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

Automated summary

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN) that converges faster than traditional methods with promising performance. This paper provides a comprehensive review on ELM, focusing on theoretical analysis, improvements for stability, efficiency, and accuracy, as well as its applications in medical imaging and discussions on controversies. The aim is to report advances and explore future perspectives.