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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)
Context weighting procedures are presented for sources with models (structures) in four diierent classes. Although the procedures are designed for universal data compression purposes, their generality allows application in the area of classiication.
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We present a method to decrease the storage and communication complexity of the context-tree weighting method. This method is based on combining the estimated probability of a node in the context tree and weighted probabilities of its children in one single variable. This variable is represented by its logarithm.
The context-tree weighting algorithm 6] is an eecient universal source coding method for tree sources. Although a nite accuracy version of this algorithm has been analysed in 8], it is better to implement the algorithm as proposed in 7]. There it was suggested to store in each node s of the context tree, instead of an estimated probability P s e and a… (More)
The sequential Context Tree Weighting procedure  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  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  R. G. Gallager, Information Theory and Reliable Communication. New York Wiley, 1968.  W. W. Peterson and E. J. Weldon, Jr., Error-Correcting… (More)
— The context-tree weighting method (Willems, Shtarkov, and Tjalkens ) is a sequential universal source coding method that achieves the Rissanen lower bound  for tree sources. The same authors also proposed context-tree maximizing, a two-pass version of the context-tree weighting method . Later Willems and Tjalkens  described a… (More)