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| Zeng, J; Tan, Zh; Matsunaga, T; Shirai, T. |
| A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a... |
| Tipo: Text |
Palavras-chave: Support vector machine for regression; SVM; LS-SVM; Machine learning; Parameter optimization; Global ocean CO2. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00676/78774/80949.pdf |
