Sequential extreme learning machine incorporating survival error potential

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

Research output: Contribution to journalArticle

7 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 languageEnglish
Pages (from-to)194-204
Number of pages11
JournalNeurocomputing
Volume155
DOIs
Publication statusPublished - 2015 May 1

Fingerprint

Learning systems
Noise
Benchmarking
Error compensation
Machine Learning
Compensation and Redress
Datasets

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Sun, Lei ; Chen, Badong ; Toh, Kar Ann ; Lin, Zhiping. / Sequential extreme learning machine incorporating survival error potential. In: Neurocomputing. 2015 ; Vol. 155. pp. 194-204.
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Sequential extreme learning machine incorporating survival error potential. / Sun, Lei; Chen, Badong; Toh, Kar Ann; Lin, Zhiping.

In: Neurocomputing, Vol. 155, 01.05.2015, p. 194-204.

Research output: Contribution to journalArticle

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