Accurate model to predict the solubility of fullerene C60 in organic solvents by using support vector regression

Fullerenes Nanotubes and Carbon Nanostructures

Volumn 25, Issue 1, 2017, Pages 58-64

  Cheng, Wen De ^a   Cai, Cong Zhong ^a  

^a CHONGQING UNIVERSITY   (China)

Author keywords

Fullerene C60; multiple regression analysis; prediction; solubility; support vector regression

Indexed keywords

ERRORS; FORECASTING; FULLERENES; GENETIC ALGORITHMS; ORGANIC SOLVENTS; PARTICLE SWARM OPTIMIZATION (PSO); SOLUBILITY; SOLVENTS; STATISTICAL METHODS;

CORRELATION COEFFICIENT; FULLERENE C; GENERALIZATION ABILITY; LEAVE-ONE-OUT CROSS-VALIDATION (LOOCV); MEAN ABSOLUTE PERCENTAGE ERROR; MULTIPLE REGRESSION ANALYSIS; PARAMETER OPTIMIZATION; SUPPORT VECTOR REGRESSION (SVR);

REGRESSION ANALYSIS;

EID: 85007381830     PISSN: 1536383X     EISSN: 15364046     Source Type: Journal    
DOI: 10.1080/1536383X.2016.1252336     Document Type: Article

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