首页|Reports from Van Lang University Advance Knowledge in Machine Learning (Optimize d Gradient Boosting Models and Reliability Analysis for Rock Stiffness C13)
Reports from Van Lang University Advance Knowledge in Machine Learning (Optimize d Gradient Boosting Models and Reliability Analysis for Rock Stiffness C13)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating from Ho Chi Minh City, V ietnam, by NewsRx editors, the research stated, “The Extreme gradient boosting a lgorithm XGBoost has been confirmed to be an accurate method for predicting rock stiffnesses and anisotropic parameters from basic input features such as rock p orosity, density, vertical compression stress, pore pressure and burial depth (N guyen-Sy, T., To, Q.D., Vu, M.N., Nguyen, T.D. and Nguyen, T.T., 2020. Study the elastic properties and the anisotropy of rocks using different machine learning methods.”
Ho Chi Minh CityVietnamAsiaCyborgsEmerging TechnologiesMachine LearningVan Lang University
NETL
NSTL
