首页|New Machine Learning Study Findings Have Been Reported by Investigators at Sheny ang University of Technology (A Machine Learning Method Based On Tpe-xgboost Mod el for Trip/twip Nearb Titanium Alloy Design)
New Machine Learning Study Findings Have Been Reported by Investigators at Sheny ang University of Technology (A Machine Learning Method Based On Tpe-xgboost Mod el for Trip/twip Nearb Titanium Alloy Design)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators publish new report on Ma chine Learning. According to news reportingout of Liaoning, People’s Republic o f China, by NewsRx editors, research stated, “The traditional designmethod for TRIP/TWIP near-beta titanium alloys is time-consuming and expensive. This paper proposesa machine-learning design method for a near-beta titanium alloy that us es the Tree-structured Parzen Estimator(TPE) algorithm to optimize the hyperpar ameter space of XGBoost for improved model predictionaccuracy in TRIP/TWIP.”
LiaoningPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesLight MetalsMachine LearningTitaniumSh enyang University of Technology
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