To compress text files,
a neural predictor network
is used to approximate the conditional probability distribution
of possible ``next characters'', given
previous characters.
's outputs are fed into standard coding algorithms
that generate short codes for characters
with high predicted probability
and long codes for highly unpredictable characters.
Tested on short German newspaper articles, our method
outperforms widely used Lempel-Ziv algorithms
(used in UNIX functions such as ``compress'' and ``gzip'').