首页|Findings from Amirkabir University of Technology Update Understanding of Machine Learning (A Comparative Study of the Xgboost Ensemble Learning and Multilayer Perceptron In Mineral Prospectivity Modeling: a Case Study of the Torud-chahshirin …)
Findings from Amirkabir University of Technology Update Understanding of Machine Learning (A Comparative Study of the Xgboost Ensemble Learning and Multilayer Perceptron In Mineral Prospectivity Modeling: a Case Study of the Torud-chahshirin …)
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 Network Daily News - Current studyresults on Machine Learning have been published. According to news originating from Tehran, Iran, byNewsRx correspondents, research stated, “Precisely selecting the exploration criteria and building robustmachine-learning models are two critical issues for enhancing the efficiency of mineral prospectivity mapping(MPM) for delimiting highly favorable mineralization zones. The efficient exploration features linked togeochemical, geological, and remote sensing criteria were distinguished in the Torud-Chahshirin (TCS)volcano-intrusive belt, NE Iran using success-rate curves.”
TehranIranCyborgsEmerging TechnologiesMachine LearningPerceptronAmirkabir University of Technology
NETL
NSTL
