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)

Robotics & Machine Learning Daily News2024，Issue(Apr.15) ：65-66.

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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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Abstract

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.”

Key words

Shijiazhuang/People’s Republic of China/Asia/Algorithms/Cyborgs/Emerging Technologies/Machine Learning/Particle S warm Optimization/Selection Algorithm/Support Vector Machines/Vector Machines/Hebei GEO University

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出版年

2024

Robotics & Machine Learning Daily News

ISSN：

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