首页|Researchers at Chinese Academy of Sciences Release New Data on Machine Learning (A Gsd-driven Approach To Deriving Stochastic Soil Strength Parameters Under Hyb rid Machine Learning Models)
Researchers at Chinese Academy of Sciences Release New Data on Machine Learning (A Gsd-driven Approach To Deriving Stochastic Soil Strength Parameters Under Hyb rid Machine Learning Models)
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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 reportingoriginating in Chengdu, People’s Rep ublic of China, by NewsRx journalists, research stated, “The quantificationof s oil strength parameters is a crucial prerequisite for constructing physical mode ls relatedto hydro-geophysical processes. However, due to ignoring soil spatial variability at different scales, traditionalparameter assignment strategies, s uch as assigning values depending on land use classification orother classifica tion systems, as well as those extrapolation and interpolation methods, are insu fficient forphysical process modelling.”
ChengduPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningPerceptronChinese Academy of Sciences
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