@Article{iasc.2020.013918,
AUTHOR = {Lihua Guo},
TITLE = {Extreme Learning Machine with Elastic Net Regularization},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {421--427},
URL = {http://www.techscience.com/iasc/v26n3/40001},
ISSN = {2326-005X},
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 to validate the efficiency of our 
system. Final experimental results indicate that our ELM-EN requires less 
training time than multi-layer perceptron, and can achieve higher recognition 
accuracy than ELM and sparse ELM.},
DOI = {10.32604/iasc.2020.013918}
}