首页|Findings from Xiangtan University Update Understanding of Machine Learning (Glas s Forming Ability Prediction of Bulk Metallic@@Glasses Based On Fused Strategy)
Findings from Xiangtan University Update Understanding of Machine Learning (Glas s Forming Ability Prediction of Bulk Metallic@@Glasses Based On Fused Strategy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingfrom Xiangtan, People’s Repub lic of China, by NewsRx correspondents, research stated, “In orderto improve th e prediction accuracy of random forest (RF), k -nearest neighbor (KNN), gradient boosteddecision trees (GBDT) and extreme gradient boosting (XGBoost) models, a fused strategy was proposedfor predicting the glass forming ability (GFA) of b ulk metallic glasses (BMGs). Feature vectors wereextracted using a trained conv olutional neural network (CNN), and alloy composition information was theonly v ariable input without requiring various physical and chemical properties acquire d from experiments.”
XiangtanPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningXiangtan University
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