Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection
暂无分享,去创建一个
[1] Michelangelo Ceci,et al. Exploiting transfer learning for the reconstruction of the human gene regulatory network , 2019, Bioinform..
[2] Wouter M. Kouw. An introduction to domain adaptation and transfer learning , 2018, ArXiv.
[3] Vincent Vercruyssen,et al. Semi-Supervised Anomaly Detection with an Application to Water Analytics , 2018, 2018 IEEE International Conference on Data Mining (ICDM).
[4] Wannes Meert,et al. Query Log Analysis: Detecting Anomalies in DNS Traffic at a TLD Resolver , 2018, DMLE/IOTSTREAMING@PKDD/ECML.
[5] Maurizio Filippone,et al. A comparative evaluation of outlier detection algorithms: Experiments and analyses , 2018, Pattern Recognit..
[6] Vincent Vercruyssen,et al. Transfer Learning for Time Series Anomaly Detection , 2017, IAL@PKDD/ECML.
[7] Jing Zhang,et al. Joint Geometrical and Statistical Alignment for Visual Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Lewis D. Griffin,et al. Transfer representation-learning for anomaly detection , 2016, ICML 2016.
[9] Seiichi Uchida,et al. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data , 2016, PloS one.
[10] Arthur Zimek,et al. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study , 2016, Data Mining and Knowledge Discovery.
[11] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[12] Jesse Davis,et al. TODTLER: Two-Order-Deep Transfer Learning , 2015, AAAI.
[13] Philip S. Yu,et al. A robust one-class transfer learning method with uncertain data , 2014, Knowledge and Information Systems.
[14] Philip S. Yu,et al. Transfer Joint Matching for Unsupervised Domain Adaptation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[15] Philip S. Yu,et al. Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.
[16] Thomas G. Dietterich,et al. Systematic construction of anomaly detection benchmarks from real data , 2013, ODD '13.
[17] Jieping Ye,et al. Multisource domain adaptation and its application to early detection of fatigue , 2012, TKDD.
[18] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Andreas Dengel,et al. Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm , 2012 .
[20] Ivor W. Tsang,et al. Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.
[21] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[22] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[23] Zhi-Hua Zhou,et al. Isolation Forest , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[24] Christos Faloutsos,et al. LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).
[25] Sridhar Ramaswamy,et al. Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD '00.
[26] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD '00.
[27] Salvatore J. Stolfo,et al. Distributed data mining in credit card fraud detection , 1999, IEEE Intell. Syst..