首页|Findings from China Three Gorges University Yields New Data on Support Vector Ma chines (Landslide Susceptibility Prediction By Gray Wolf Optimized Support Vecto r Machine Model Under Different Factor States)
Findings from China Three Gorges University Yields New Data on Support Vector Ma chines (Landslide Susceptibility Prediction By Gray Wolf Optimized Support Vecto r Machine Model Under Different Factor States)
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NETL
NSTL
Spie-Soc Photo-Optical Instrumentation Engineers
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Support Vector Machine s is the subject of a report. According to news originating from Yichang, People ’s Republic of China, by NewsRx correspondents, research stated, “Landslide susc eptibility prediction (LSP) is crucial for hazard prevention and geological risk assessment. Support vector machine (SVM) is widely used for LSP, but its parame ter optimization problem affects the prediction accuracy and generalization abil ity of the model, and variations in parameter combinations may result in differe nt prediction outcomes, which brings some challenges to the application of the m odel.”
YichangPeople’s Republic of ChinaAsi aEmerging TechnologiesMachine LearningSupport Vector MachinesVector Mach inesChina Three Gorges University
NETL
NSTL
Spie-Soc Photo-Optical Instrumentation Engineers
