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Improved Extreme Learning Machine and Its Application in Image Quality Assessment

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  • Li Mao
  • Lidong Zhang
  • Xingyang Liu
  • Chaofeng Li
  • Hong Yang

Abstract

Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted) ELM, and propose a modified (weighted) extreme learning machine (M-ELM and M-WELM). Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.

Suggested Citation

  • Li Mao & Lidong Zhang & Xingyang Liu & Chaofeng Li & Hong Yang, 2014. "Improved Extreme Learning Machine and Its Application in Image Quality Assessment," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, May.
    • Handle: RePEc:hin:jnlmpe:426152
    DOI: 10.1155/2014/426152
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    Cited by:

    1. Xiyong Zhao & Yanzhou Li & Yongli Chen & Xi Qiao, 2022. "A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images," Sustainability, MDPI, vol. 14(19), pages 1-15, October.

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