首页|Findings from Beijing Information Science and Technology University Reveals New Findings on Support Vector Machines (A Pipeline Leakage Aperture Identification Method Based On Cnn-svm Considering Sample Granularity)
Findings from Beijing Information Science and Technology University Reveals New Findings on Support Vector Machines (A Pipeline Leakage Aperture Identification Method Based On Cnn-svm Considering Sample Granularity)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Machine Learn ing - Support Vector Machines have beenpublished. According to news reporting o riginating from Beijing, People’s Republic of China, by NewsRxcorrespondents, r esearch stated, “The accurate identification of pipeline leakage apertures is cr ucialfor safeguarding the environment and conserving resources. This article pr oposes a novel approach foridentifying pipeline leakage apertures through the f usion of convolutional neural network and supportvector machine (CNN-SVM).”
BeijingPeople’s Republic of ChinaAsi aMachine LearningSupport Vector MachinesBeijing Information Science and Te chnology University
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