首页|Study Findings from Nanchang University Broaden Understanding of Machine Learnin g (Modelling Landslide Susceptibility Prediction: a Review and Construction of S emi-supervised Imbalanced Theory)
Study Findings from Nanchang University Broaden Understanding of Machine Learnin g (Modelling Landslide Susceptibility Prediction: a Review and Construction of S emi-supervised Imbalanced Theory)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news originating from Nanchang, People’s Republic of China, by NewsRx correspondents, research stated, “Fully supervised machine learning models are widely applied for landslide susceptibility prediction (LSP) , mainly using landslide and non -landslide samples as output variables and rela ted conditioning factors as input variables. However, there are many uncertain i ssues in LSP modelling; for example, known landslide samples may have errors, no n -landslide samples randomly selected from the whole study area are not accurat e, the ratio of landslide to non -landslide samples set as 1:1 is not consistent with the actual landslide distribution characteristics, it is unreasonable to a ssign samples labelled non -landslide a probability of 0, and it is difficult to achieve a comprehensive assessment of LSP performance.”
NanchangPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningNanchang University
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