Spatial Representation for Efficient Sequence Classification

  title={Spatial Representation for Efficient Sequence Classification},
  author={Pavel P. Kuksa and Vladimir Pavlovic},
  journal={2010 20th International Conference on Pattern Recognition},
We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently introduced for the problem of biological sequence classification, exploits a novel multi-scale representation of strings. The new representation leads to discovery of very efficient algorithms for string comparison, independent of the alphabet size. We show that these algorithms can be generalized to handle a wide… CONTINUE READING
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Publications referenced by this paper.
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2008 19th International Conference on Pattern Recognition • 2008
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