Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy.

Chen, Huazhou; Xu, Lili; Ai, Wu; Lin, Bin; Feng, Quanxi; Cai, Ken

Chen, Huazhou; Xu, Lili; Ai, Wu; Lin, Bin; Feng, Quanxi; Cai, Ken.

Afiliación

Chen H; College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China.
Xu L; College of Marine Sciences, Beibu Gulf University, Qinzhou 535011, China.
Ai W; College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China.
Lin B; College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China.
Feng Q; College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China.
Cai K; College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China. Electronic address: kencaizhku@foxmail.com.

Sci Total Environ ; 714: 136765, 2020 Apr 20.

Article en En | MEDLINE | ID: mdl-31982759

Palabras clave

Kernel functions; Least squares support vector machine; Logistic-based network; Near-infrared spectroscopy; Water pollution

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Total Environ Año: 2020 Tipo del documento: Article País de afiliación: China

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