document: Online novelty detection on temporal sequences 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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