Robust regularized extreme learning machine for regression with non-convex loss function via DC program

DOI10.1016/j.jfranklin.2020.05.027zbMath1497.68443OpenAlexW3031535528MaRDI QIDQ2200186

Ping Zhong, Kuaini Wang, Cao, Jinde, Hui-Min Pei

Publication date: 15 September 2020

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2020.05.027

zbMATH Keywords

efficiencyELMdata-driven modelingRRELM

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of mathematical programming (90C90) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

A novel neural network to nonlinear complex-variable constrained nonconvex optimization

Uses Software

• OP-ELM

Cites Work

• The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
• Weighted least squares support vector machines: robustness and sparse approximation
• Robust support vector regression in the primal
• A survey of outlier detection methodologies
• On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
• Training robust support vector regression with smooth non-convex loss function
• Support Vector Machines
• A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem
• The Concave-Convex Procedure
• Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses
• Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery
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