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- W2610082701 abstract "On-line supervised spotting and classification of subsequences can be performed by comparing some distance between the stream and previously learnt time series. However, learning a few incorrect time series can trigger disproportionately many false alarms. In this paper, we propose a fast technique to prune bad instances away and automatically select appropriate distance thresholds. Our main contribution is to turn the ill-defined spotting problem into a collection of single well-defined binary classification problems, by segmenting the stream and by ranking subsets of instances on those segments very quickly. We further demonstrate our technique's effectiveness on a gesture recognition application." @default.
- W2610082701 created "2017-05-05" @default.
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- W2610082701 date "2016-12-01" @default.
- W2610082701 modified "2023-09-25" @default.
- W2610082701 title "Detecting low-quality reference time series in stream recognition" @default.
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- W2610082701 doi "https://doi.org/10.1109/icpr.2016.7900021" @default.
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