Art

J-GLOBAL ID：202002233008355114   Reference number：20A1809286

Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

セルラネットワークにおける半教師付き機械学習支援異常検出法【JST・京大機械翻訳】

Author (7)： Lu Yutao (FocusLab, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China) ,  Wang Juan (FocusLab, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China) ,  Liu Miao (FocusLab, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China) ,  Zhang Kaixuan (FocusLab, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China) ,  Gui Guan (FocusLab, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China) ,  Ohtsuki Tomoaki (Department of Information and Computer Science, Keio University, Yokohama, Japan) ,  Adachi Fumiyuki (Research Organization of Electrical Communication, Tohoku University, Sendai, Japan)
Material： IEEE Transactions on Vehicular Technology  (IEEE Trans Veh Technol)
Volume： 69  Issue： 8  Page： 8459-8467  Publication year： 2020 
JST Material Number： C0244A  ISSN： 0018-9545  CODEN： ITVTAB  Document type： Article
Article type： 原著論文  Country of issue： United States (USA)  Language： ENGLISH (EN)

Abstract/Point： The ever-increasing amount of ...

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Thesaurus term： algorithm ,  operator (worker) ,  *network ,  *Machine Learning ,  anomaly detection ,  *cellular network ,  quality index ,  supervised machine learning
Semi thesaurus term： 体感品質 , 【Automatic Indexing@JST】

JST classification (1)： Mobile communication  (ND08030H)

Terms in the title (4)： セルラネットワーク ,  教師付き機械学習 ,  支援 ,  異常検出