Probabilistic two-level anomaly detection for correlated systems

Bin Tong, Tetsuro Morimura, Einoshin Suzuki, Tsuyoshi Idé

研究成果: 著書/レポートタイプへの貢献会議での発言

抜粋

We propose a novel probabilistic semi-supervised anomaly detection framework for multi-dimensional systems with high correlation among variables. Our method is able to identify both abnormal instances and abnormal variables of an instance.

元の言語英語
ホスト出版物のタイトルECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
編集者Torsten Schaub, Gerhard Friedrich, Barry O'Sullivan
出版者IOS Press
ページ1109-1110
ページ数2
ISBN(電子版)9781614994183
DOI
出版物ステータス出版済み - 1 1 2014
イベント21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, チェコ共和国
継続期間: 8 18 20148 22 2014

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
263
ISSN(印刷物)0922-6389

その他

その他21st European Conference on Artificial Intelligence, ECAI 2014
チェコ共和国
Prague
期間8/18/148/22/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

フィンガープリント Probabilistic two-level anomaly detection for correlated systems' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Tong, B., Morimura, T., Suzuki, E., & Idé, T. (2014). Probabilistic two-level anomaly detection for correlated systems. : T. Schaub, G. Friedrich, & B. O'Sullivan (版), ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings (pp. 1109-1110). (Frontiers in Artificial Intelligence and Applications; 巻数 263). IOS Press. https://doi.org/10.3233/978-1-61499-419-0-1109