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Extreme learning machines: a survey

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Abstract

Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.

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Acknowledgments

This research was sponsored by the grant from Academic Research Fund (AcRF) Tier 1 under project no. RG 22/08 (M52040128).

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  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Guang-Bin Huang & Yuan Lan

  2. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, 3086, Australia

    Dian Hui Wang

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Huang, GB., Wang, D.H. & Lan, Y. Extreme learning machines: a survey. Int. J. Mach. Learn. & Cyber. 2, 107–122 (2011). https://doi.org/10.1007/s13042-011-0019-y

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