Normalized Compression Distance of Multisets with Applications

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@Article{Cohen:2015:ieeeTPAMI,
  author =       "Andrew R. Cohen and Paul M. B. Vitanyi",
  title =        "Normalized Compression Distance of Multisets with
                 Applications",
  journal =      "IEEE Transactions on Pattern Analysis and Machine
                 Intelligence",
  year =         "2015",
  volume =       "37",
  number =       "8",
  pages =        "1602--1614",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0162-8828",
  DOI =          "doi:10.1109/TPAMI.2014.2375175",
  abstract =     "Pairwise normalized compression distance (NCD) is a
                 parameter-free, feature-free, alignment-free,
                 similarity metric based on compression. We propose an
                 NCD of multisets that is also metric. Previously,
                 attempts to obtain such an NCD failed. For
                 classification purposes it is superior to the pairwise
                 NCD in accuracy and implementation complexity. We cover
                 the entire trajectory from theoretical underpinning to
                 feasible practice. It is applied to biological (stem
                 cell, organelle transport) and OCR classification
                 questions that were earlier treated with the pairwise
                 NCD. With the new method we achieved significantly
                 better results. The theoretic foundation is Kolmogorov
                 complexity.",
  notes =        "Also known as \cite{6967789}",
}

Genetic Programming entries for Andrew R Cohen Paul M B Vitanyi

Citations