Robust stochastic configuration networks with kernel density estimation for uncertain data regression

DOI10.1016/j.ins.2017.05.047zbMath1443.62315arXiv1702.04459OpenAlexW2618410756MaRDI QIDQ778389

Dianhui Wang, Ming Li

Publication date: 2 July 2020

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

Full work available at URL: https://arxiv.org/abs/1702.04459

zbMATH Keywords

randomized algorithmskernel density estimationalternating optimization techniquesrobust data regressionstochastic configuration networks

Mathematics Subject Classification ID

Nonparametric robustness (62G35) General nonlinear regression (62J02) Neural nets and related approaches to inference from stochastic processes (62M45)

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Cites Work

• A probabilistic learning algorithm for robust modeling using neural networks with random weights
• Weighted least squares support vector machines: robustness and sparse approximation
• Approximation with random bases: pro et contra
• Insights into randomized algorithms for neural networks: practical issues and common pitfalls
• Robust Statistics
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