We consider re-representing the alphabet so that a representation of a character
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.
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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