A robust regression based on weighted LSSVM and penalized trimmed squares

DOI10.1016/j.chaos.2015.12.012zbMath1360.62374OpenAlexW2238263256MaRDI QIDQ528415

Jianyong Liu, Jie Guo, Qin Yu, Chengqun Fu, Yong Wang

Publication date: 12 May 2017

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2015.12.012

zbMATH Keywords

outlier eliminatepenalized trimmed squaresweighted LS-SVM

Mathematics Subject Classification ID

General nonlinear regression (62J02)

Related Items (1)

Robust regression using support vector regressions

Cites Work

• A fast algorithm for robust regression with penalised trimmed squares
• Quadratic mixed integer programming and support vectors for deleting outliers in robust regression
• The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators
• Weighted least squares support vector machines: robustness and sparse approximation
• Deleting outliers in robust regression with mixed integer programming
• Support Vector Machine Regression Algorithm Based on Chunking Incremental Learning
• Chaos control using least-squares support vector machines
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