Detecting emerging and evolving novelties with locally adaptive density ratio estimation-Reference-Cited by-同舟云学术

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Detecting emerging and evolving novelties with locally adaptive density ratio estimation

Published:2016-03-05 Issue:3 Volume:49 Page:1131-1159
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Miao Yun-Qian,Farahat Ahmed K.,Kamel Mohamed S.

Funder

Ontario Graduate Scholarship

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10115-016-0929-9.pdf

Reference38 articles.

1. Aggarwal CC, Yu PS (2008) Outlier detection with uncertain data. In: Proceedings of the SIAM international conference on data mining (SDM). SIAM, pp 483–493

2. Bellman R (1970) Introduction to matrix analysis, vol 960. SIAM. McGraw-Hill, New York

3. Branch JW, Giannella C, Szymanski B, Wolff R, Kargupta H (2013) In-network outlier detection in wireless sensor networks. Knowl Inf Syst 34(1):23–54

4. Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: ACM sigmod record, vol 29. ACM, pp 93–104

5. Chandola V, Banerjee A, Kumar V (2012) Anomaly detection for discrete sequences: a survey. IEEE Trans Knowl Data Eng 24(5):823–839

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