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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Auteurs principaux:	Ziyi Chen, Wenbai Liu
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2020-01-01
Collection:	IEEE Access
Sujets:	Machine learning forecasting problem K-means clustering method chaotic slime mould algorithm support vector regression
Accès en ligne:	https://ieeexplore.ieee.org/document/9174730/
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