Normalized Compression Distance of Multisets with Applications
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- @Article{Cohen:2015:ieeeTPAMI,
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author = "Andrew R. Cohen and Paul M. B. Vitanyi",
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title = "Normalized Compression Distance of Multisets with
Applications",
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journal = "IEEE Transactions on Pattern Analysis and Machine
Intelligence",
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year = "2015",
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volume = "37",
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number = "8",
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pages = "1602--1614",
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month = aug,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0162-8828",
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DOI = "doi:10.1109/TPAMI.2014.2375175",
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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.",
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notes = "Also known as \cite{6967789}",
- }
Genetic Programming entries for
Andrew R Cohen
Paul M B Vitanyi
Citations