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Abstract: Novelty detection, or anomaly detection, on temporal sequences has increasingly attracted attention from researchers in different areas. In this paper, we present a new framework for online novelty detection on temporal sequences . This framework includes a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm.... (Update)
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BibTeX entry: (Update)
Ma, J., and Perkins, S. Online Novelty Detection on Temporal Sequences. In Proc. of the Ninth ACM SIGKDD, ACM Press. 613-618. 2003 http://citeseer.ist.psu.edu/ma03online.html More
@misc{ ma03online,
author = "J. Ma and S. Perkins",
title = "Online Novelty Detection on Temporal Sequences",
text = "Ma, J., and Perkins, S. Online Novelty Detection on Temporal Sequences.
In Proc. of the Ninth ACM SIGKDD, ACM Press. 613-618. 2003",
year = "2003",
url = "citeseer.ist.psu.edu/ma03online.html" }
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16 Finding Surprising Patterns in a Time Series Database In Lin.. - Keogh, Lonardi et al. - 2002
14 A Linear Programming Approach to Novelty Detection - Colin, Bennett - 2001
13 Tsa-tree: A Waveletbased Approach to Improve the Efficiency .. - Tian, Zhao - 2000
8 Novelty Detection Using Self-Organizing Maps - Alexander, Duin - 1997
7 Support vector method for novelty detection (context) - Schlkopf, Williamson et al. - 2000
4 Anomaly Detection by Neural Network Models and Statistical T.. (context) - Kozma, Kitamura et al. - 1994
4 Classification and Novelty Detection Using Linear Models and.. (context) - Tom, Johnson et al. - 1998
2 A Mixture Approach to Novelty Detection Using Training Data .. - Martin - 2001
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1 Accurate Online Support Vector Regression - Junshui, Theiler et al. - 2003
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