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In this paper, we present a new framework for online novelty detection on temporal sequences. This framework include 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. Experiments on both synthetic and real world data are performed to demonstrate the promising performance of our proposed detection algorithm.
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Los Alamos National Laboratory
AureoGen Biosciences (United States)
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Ma et al. (Sun,) studied this question.
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