Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1)
  • Open Access

    ARTICLE

    Extreme Learning Machine with Elastic Net Regularization

    Lihua Guo*

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 421-427, 2020, DOI:10.32604/iasc.2020.013918

    Abstract Compared with deep neural learning, the extreme learning machine (ELM) can be quickly converged without iteratively tuning hidden nodes. Inspired by this merit, an extreme learning machine with elastic net regularization (ELM-EN) is proposed in this paper. The elastic net is a regularization method that combines LASSO and ridge penalties. This regularization can keep a balance between system stability and solution's sparsity. Moreover, an excellent optimization method, i.e., accelerated proximal gradient, is used to find the minimum of the system optimization function. Various datasets from UCI repository and two facial expression image datasets are used More >

Displaying 1-10 on page 1 of 1. Per Page