On fixed-database universal data compression with limited memory

The amount of fixed side information required for lossless data compression is discussed. Nonasymptotic coding and converse theorems are derived for data-compression algorithms with fixed statistical side information ("training sequence") that is not large enough so as to yield the ultimate compression, namely, the entropy of the source.