首页|Reports Summarize Support Vector Machines Findings from South China University o f Technology (Regional Fault Location of Distribution Network Based On Distribut ed Observation and Fusion of Multi-source Evidence)
Reports Summarize Support Vector Machines Findings from South China University o f Technology (Regional Fault Location of Distribution Network Based On Distribut ed Observation and Fusion of Multi-source Evidence)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning - Support Vector Machines. Accordingto news reporting out of Guangzho u, People’s Republic of China, by NewsRx editors, research stated, “Thispaper p roposes a multi-source evidence generation strategy (MEGS) that utilises distrib uted measurementsto train a multi-classification support vector machine (SVM) f or each observer. An observer employstime-frequency analysis to transform local current signals into feature samples, which serve as inputs tothe SVM.”
GuangzhouPeople’s Republic of ChinaA siaMachine LearningSupport Vector MachinesSouth China University of Techno logy
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