Sequential extreme learning machine incorporating survival error potential

Lei Sun; Badong Chen; Kar Ann Toh; Zhiping Lin

doi:10.1016/j.neucom.2014.12.029

Sequential extreme learning machine incorporating survival error potential

Lei Sun, Badong Chen, Kar Ann Toh^*, Zhiping Lin

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error tolerant and stable solution is obtained. The developed method is tested on chaotic time sequence as well as benchmark data sets. Experimental results show potential applications for the developed method.

Original language	English
Pages (from-to)	194-204
Number of pages	11
Journal	Neurocomputing
Volume	155
DOIs	https://doi.org/10.1016/j.neucom.2014.12.029
Publication status	Published - 1 May 2015

Keywords

Extreme learning machine
Information measure
Sequential learning machine

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10.1016/j.neucom.2014.12.029

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Sun, L., Chen, B., Toh, K. A., & Lin, Z. (2015). Sequential extreme learning machine incorporating survival error potential. Neurocomputing, 155, 194-204. https://doi.org/10.1016/j.neucom.2014.12.029

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abstract = "A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error tolerant and stable solution is obtained. The developed method is tested on chaotic time sequence as well as benchmark data sets. Experimental results show potential applications for the developed method.",

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AB - A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error tolerant and stable solution is obtained. The developed method is tested on chaotic time sequence as well as benchmark data sets. Experimental results show potential applications for the developed method.

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KW - Information measure

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Sequential extreme learning machine incorporating survival error potential

Abstract

Keywords

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