Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this...

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Bibliographic Details
Main Authors: Weidong Cao, Xin Liu, Jianjun Ni
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Machine learning
prediction problems
support vector regression (SVR)
henry gas solubility optimization (HGSO)
Online Access:https://ieeexplore.ieee.org/document/9090132/

Internet

https://ieeexplore.ieee.org/document/9090132/

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