Noise resistant training for extreme learning machine

Yik Lam LUI; Hiu Tung WONG; Chi-Sing LEUNG; Sam KWONG

doi:10.1007/978-3-319-59081-3_31

Noise resistant training for extreme learning machine

Yik Lam LUI, Hiu Tung WONG, Chi-Sing LEUNG, Sam KWONG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	257-265
DOIs	https://doi.org/10.1007/978-3-319-59081-3_31
Publication status	Published - 2017
Externally published	Yes

Bibliographical note

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

Keywords

Extreme learning machines
Incremental algorithm
Node noise

Access to Document

10.1007/978-3-319-59081-3_31

Cite this

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title = "Noise resistant training for extreme learning machine",

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.",

keywords = "Extreme learning machines, Incremental algorithm, Node noise",

author = "LUI, {Yik Lam} and WONG, {Hiu Tung} and Chi-Sing LEUNG and Sam KWONG",

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

year = "2017",

doi = "10.1007/978-3-319-59081-3_31",

language = "English",

pages = "257--265",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Noise resistant training for extreme learning machine. / LUI, Yik Lam; WONG, Hiu Tung; LEUNG, Chi-Sing et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 257-265.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Noise resistant training for extreme learning machine

AU - LUI, Yik Lam

AU - WONG, Hiu Tung

AU - LEUNG, Chi-Sing

AU - KWONG, Sam

N1 - The work was supported by a research grant from the Government of the Hong Kong Special Administrative Region (CityU 11259516).

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Extreme learning machines

KW - Incremental algorithm

KW - Node noise

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U2 - 10.1007/978-3-319-59081-3_31

DO - 10.1007/978-3-319-59081-3_31

M3 - Conference paper (refereed)

SP - 257

EP - 265

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Noise resistant training for extreme learning machine

Abstract

Bibliographical note

Keywords

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