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dc.contributor.authorLong, Philip M.
dc.contributor.authorNatsev, Apostol
dc.contributor.authorVitter, Jeffrey Scott
dc.date.accessioned2011-03-21T19:10:16Z
dc.date.available2011-03-21T19:10:16Z
dc.date.issued1999
dc.identifier.citationP. 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
dc.identifier.urihttp://hdl.handle.net/1808/7215
dc.description.abstractWe 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.
dc.language.isoen_US
dc.publisherElsevier
dc.titleText Compression Via Alphabet Re-Representation
dc.typeArticle
kusw.kuauthorVitter, Jeffrey Scott
kusw.oastatusfullparticipation
dc.identifier.doi10.1016/S0893-6080(99)00022-2
kusw.oaversionScholarly/refereed, author accepted manuscript
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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