首页|Researchers from Hebei GEO University Report Findings in Support Vector Machines (Comparison of Debris Flow Susceptibility Assessment Methods: Support Vector Ma chine, Particle Swarm Optimization, and Feature Selection Techniques)
Researchers from Hebei GEO University Report Findings in Support Vector Machines (Comparison of Debris Flow Susceptibility Assessment Methods: Support Vector Ma chine, Particle Swarm Optimization, and Feature Selection Techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Investigators discuss new findings in Support Vec tor Machines. According to news originatingfrom Shijiazhuang, People’s Republic of China, by NewsRx correspondents, research stated, “The selectionof importan t factors in machine learning-based susceptibility assessments is crucial to obt ain reliablesusceptibility results. In this study, metaheuristic optimization a nd feature selection techniques wereapplied to identify the most important inpu t parameters for mapping debris flow susceptibility in thesouthern mountain are a of Chengde City in Hebei Province, China, by using machine learning algorithms.”
ShijiazhuangPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningParticle S warm OptimizationSelection AlgorithmSupport Vector MachinesVector MachinesHebei GEO University
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