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    Text Compression Via Alphabet Re-Representation

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    LNV97.re-representation.pdf (246.1Kb)
    Issue Date
    1999
    Author
    Long, Philip M.
    Natsev, Apostol
    Vitter, Jeffrey Scott
    Publisher
    Elsevier
    Type
    Article
    Article Version
    Scholarly/refereed, author accepted manuscript
    Metadata
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    Abstract
    We consider re-representing the alphabet so that a representation of a character re ects its properties as a predictor of future text. This enables us to use an estimator from a restricted class to map contexts to predictions of upcoming characters. We describe an algorithm that uses this idea in conjunction with neural networks. The performance of this implementation is compared to other compression methods, such as UNIX compress, gzip, PPMC, and an alternative neural network approach.
    URI
    http://hdl.handle.net/1808/7215
    DOI
    https://doi.org/10.1016/S0893-6080(99)00022-2
    Collections
    • Distinguished Professors Scholarly Works [918]
    • Electrical Engineering and Computer Science Scholarly Works [306]
    • Provost Office Published Articles [95]
    Citation
    P. M. Long, A. I. Natsev, and J. S. Vitter. “Text Compression Via Alphabet Re-Representation,” Neural Networks, 12 (4–5), 1999, 755–765. An extended abstract appears in Proceedings of the 1997 IEEE Data Compression Conference (DCC ’97), Snowbird, UT, March 1997. http://dx.doi.org/10.1016/S0893-6080(99)00022-2

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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