Parameter selection of support vector regression based on hybrid optimization algorithm and its application

DOI10.1007/S11768-005-0026-1zbMath1284.93270OpenAlexW2043829975MaRDI QIDQ5408964

Xin Wang, Bin Qin, Chun Hua Yang, Wei Hua Gui

Publication date: 11 April 2014

Published in: Journal of Control Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11768-005-0026-1

zbMATH Keywords

support vector regressiongenetic algorithm (GA)hybrid optimizationparameters tuning

Mathematics Subject Classification ID

Uses Software

• Genocop
• GAToolBox

Cites Work

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• Practical selection of SVM parameters and noise estimation for SVM regression
• Decomposition Methods for Linear Support Vector Machines
• Efficient SVM regression training with SMO
• Feasible direction decomposition algorithms for training support vector machines

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