An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm
Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a r...
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| 主要な著者: | , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
IEEE
2020-01-01
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| シリーズ: | IEEE Access |
| 主題: | |
| オンライン･アクセス: | https://ieeexplore.ieee.org/document/9174730/ |
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