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We describe a sequential universal data compression procedure for binary tree sources that performs the \double mixture". Using a context tree, this method weights in an eecient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and(More)
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The sequential Context Tree Weighting procedure [6] achieves the asymptotically optimal redundancy behavior of k/2 · (log n)/n, where k is the number of free parameters of the source and n is the sequence length. For FSMX sources with an alphabet A, this number, k, is (|A| − 1) · |K a |. However, especially the FSMX sources often use only a few possible(More)
TABLE I EXAMPLE CODEWORDS AND Q‘, Q2, AND Q3 [7] P. Delsarte and P. Piret, “Algebraic constructions of Shannon codes for regular channels,” IEEE Trans. Inform. Theory, vol. IT-28, no. 4, pp. 593-599. Q’ Q2 Q 3 [8] R. G. Gallager, Information Theory and Reliable Communication. New York Wiley, 1968. [9] W. W. Peterson and E. J. Weldon, Jr., Error-Correcting(More)
— The context-tree weighting method (Willems, Shtarkov, and Tjalkens [1995]) is a sequential universal source coding method that achieves the Rissanen lower bound [1984] for tree sources. The same authors also proposed context-tree maximizing, a two-pass version of the context-tree weighting method [1993]. Later Willems and Tjalkens [1998] described a(More)