首页|Research Data from University of California Los Angeles (UCLA) Update Understand ing of Machine Learning (Machine-learning Based Identification of the Critical D riving Factors Controlling Storm-time Outer Radiation Belt Electron Flux Dropout s)
Research Data from University of California Los Angeles (UCLA) Update Understand ing of Machine Learning (Machine-learning Based Identification of the Critical D riving Factors Controlling Storm-time Outer Radiation Belt Electron Flux Dropout s)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Los Angeles, California, by NewsRx journalists, research stated, “Understanding and forecasting outer ra diation belt electron flux dropouts is one of the top concerns in space physics. By constructing Support Vector Machine (SVM) models to predict storm-time dropo uts for both relativistic and ultrarelativistic electrons over L = 4.0-6.0, we investigate the nonlinear correlations between various driving factors (model in puts) and dropouts (model output) and rank their relative importance.” Funders for this research include National Aeronautics & Space Adm inistration (NASA), Van Allen Probes mission, University of Colorado Boulder und er NASA, NSF - Directorate for Engineering (ENG).
Los AngelesCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSup port Vector MachinesVector MachinesUniversity of California Los Angeles (UCL A)
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