Jump to content

Context tree weighting

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by Citation bot (talk | contribs) at 14:30, 15 April 2021 (Alter: pages, title. Add: citeseerx, doi, journal. Removed parameters. Formatted dashes. | Use this bot. Report bugs. | Suggested by AManWithNoPlan | Category:CS1 maint: ref=harv | via #UCB_Category 561/2500). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

The context tree weighting method (CTW) is a lossless compression and prediction algorithm by Willems, Shtarkov & Tjalkens 1995. The CTW algorithm is among the very few such algorithms that offer both theoretical guarantees and good practical performance (see, e.g. Begleiter, El-Yaniv & Yona 2004). The CTW algorithm is an “ensemble method,” mixing the predictions of many underlying variable order Markov models, where each such model is constructed using zero-order conditional probability estimators.

References

  • Willems; Shtarkov; Tjalkens (1995), "The Context-Tree Weighting Method: Basic Properties", IEEE Transactions on Information Theory, 41 (3), IEEE Transactions on Information Theory: 653–664, doi:10.1109/18.382012
  • Willems; Shtarkov; Tjalkens (1997), Reflections on "The Context-Tree Weighting Method: Basic Properties", vol. 47, IEEE Information Theory Society Newsletter, CiteSeerX 10.1.1.109.1872{{citation}}: CS1 maint: location missing publisher (link)
  • Begleiter; El-Yaniv; Yona (2004), On Prediction Using Variable Order Markov Models, vol. 22, Journal of Artificial Intelligence Research: Journal of Artificial Intelligence Research, pp. 385–421