Prediction of X-ray fluorescence copper grade using regularized stochastic configuration networks

DOI10.1016/j.ins.2024.120098OpenAlexW4390618335MaRDI QIDQ6197209

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Publication date: 16 February 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2024.120098

zbMATH Keywords

regularization modelstochastic configuration networksgeneralized M-estimationindustrial data modeling

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

• Unnamed Item
• Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression
• Robust stochastic configuration networks with kernel density estimation for uncertain data regression
• Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation
• Approximation with random bases: pro et contra

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