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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9090132/ |