首页|Reports Summarize Machine Learning Findings from Northeastern University (An Unsupervised Machine Learning Approach for Ground-motion Spectra Clustering and Selection)
Reports Summarize Machine Learning Findings from Northeastern University (An Unsupervised Machine Learning Approach for Ground-motion Spectra Clustering and Selection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. According to newsreporting originating in Boston, Massachusetts, by NewsRx journalists, research stated, “Clustering analysisof sequence data continues to address many applications in engineering design, aided with the rapid growthof machine learning in applied science. This paper presents an unsupervised machine learning algorithm toextract defining characteristics of earthquake ground-motion spectra, also called latent features, to aid inground-motion selection (GMS).”
BostonMassachusettsUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningNortheastern University
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