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 >
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