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A Bag-of-Features Framework to Classify Time Series
2013
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time series classification is an important task with many challenging applications. Nearest-neighbor classifiers with Dynamic Time Warping (DTW) distance is a strong solution in this context, but its performance degrades with long time series, relatively short features of interest, and moderate noise. On the other hand, feature-based ap-* Corresponding author. Email: mbaydoga@asu.edu c 2012 IEEE 1 easily integrated through a fast, efficient learner that handles mixed data types, different
doi:10.1109/tpami.2013.72
pmid:24051736
fatcat:pxwoupvonnb2rno545kbwizm34