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Noise Resistant Training for Extreme Learning Machine

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

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Abstract

The extreme learning machine (ELM) concept provides some effective training algorithms to construct single hidden layer feedforward networks (SHLFNs). However, the conventional ELM algorithms were designed for the noiseless situation only, in which the outputs of the hidden nodes are not contaminated by noise. This paper presents two noise-resistant training algorithms, namely noise-resistant incremental ELM (NRI-ELM) and noise-resistant convex incremental ELM (NRCI-ELM). For NRI-ELM, its noise-resistant ability is better than that of the conventional incremented ELM algorithms. To further enhance the noise resistant ability, the NRCI-ELM algorithm is proposed. The convergent properties of the two proposed noise resistant algorithms are also presented.

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Acknowledgment

The work was supported by a research grant from the Government of the Hong Kong Special Administrative Region (CityU 11259516).

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Authors and Affiliations

  1. City University of Hong Kong, Kowloon Tong, Hong Kong

    Yik Lam Lui, Hiu Tung Wong, Chi-Sing Leung & Sam Kwong

Authors
  1. Yik Lam Lui
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  2. Hiu Tung Wong
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  3. Chi-Sing Leung
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  4. Sam Kwong
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Corresponding author

Correspondence to Chi-Sing Leung .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Lui, Y.L., Wong, H.T., Leung, CS., Kwong, S. (2017). Noise Resistant Training for Extreme Learning Machine. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_31

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  • DOI: https://doi.org/10.1007/978-3-319-59081-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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