Abstract
Nonasymptotic coding and converse theorems are derived for universal data compression algorithms in cases where the training sequence (history) that is available to the encoder consists of the most recent segment of the input data string that has been processed, but is not large enough so as to yield the ultimate compression, namely, the entropy of the source.
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