首页|Qingdao University Reports Findings in Support Vector Machines (Predicting anti- trypanosome effect of carbazole-derived compounds by powerful SVM with novel ker nel function and comprehensive learning PSO)
Qingdao University Reports Findings in Support Vector Machines (Predicting anti- trypanosome effect of carbazole-derived compounds by powerful SVM with novel ker nel function and comprehensive learning PSO)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Support Vector Machine s is the subject of a report. According to news reporting from Qingdao, People’s Republic of China, by NewsRx journalists, research stated, “In order to predict the anti-trypanosome effect of carbazole-derived compounds by quantitative stru cture-activity relationship, five models were established by the linear method, random forest, radial basis kernel function support vector machine, linear combi nation mix-kernel function support vector machine, and nonlinear combination mix -kernel function support vector machine (NLMIX-SVM). The heuristic method and op timized CatBoost were used to select two different key descriptor sets for build ing linear and nonlinear models, respectively.”
QingdaoPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Mach ines
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