A review on extreme learning machine

Jian Wang, Siyuan Lu, Shuihua Wang‎, Yudong Zhang

2021Multimedia Tools and Applications536 citationsDOI Open Access PDF

Abstract

Abstract 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.

Topics & Concepts

Extreme learning machineComputer scienceGeneralizationArtificial intelligenceStability (learning theory)Machine learningCluster analysisFocus (optics)Feedforward neural networkArtificial neural networkFeed forwardMathematicsControl engineeringOpticsPhysicsMathematical analysisEngineeringMachine Learning and ELMNeural Networks and ApplicationsDomain Adaptation and Few-Shot Learning