Context tree weighting: Difference between revisions

Content deleted Content added

Inline

Latest revision as of 05:51, 12 April 2024

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", Journal of Artificial Intelligence Research, 22, Journal of Artificial Intelligence Research: 385–421, arXiv:1107.0051, doi:10.1613/jair.1491, S2CID 47180476

External links

This computer science article is a stub. You can help Wikipedia by expanding it.

@@ Line 1: / Line 1: @@
-The '''context tree weighting method''' (CTW) is a [[lossless compression]] and prediction algorithm. The CTW algorithm is among the very few such algorithms that offer both theoretical guarantees and good practical performance.{{fact}}
+The '''context tree weighting method''' ('''CTW''') is a [[lossless compression]] and prediction algorithm by {{harvnb|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. {{harvnb|Begleiter|El-Yaniv|Yona|2004}}).
+The CTW algorithm is an “[[Ensemble learning|ensemble method]]”, mixing the predictions of many underlying variable order [[Markov model]]s, where each such model is constructed using zero-order conditional probability ''estimators''.
-The CTW algorithm is an “ensemble method,” mixing the predictions of many underlying variable order [[Markov model]]s, where each such model is constructed using zero-order conditional probability estimators.
+== References ==
+* {{Citation
+ | last1=Willems
+ | last2=Shtarkov
+ | last3=Tjalkens
+ | year=1995
+ | title=The Context-Tree Weighting Method: Basic Properties
+ | journal=IEEE Transactions on Information Theory
+ | publication-place=IEEE Transactions on Information Theory
+ | volume=41
+ | number=3
+ | pages=653–664
+ | doi=10.1109/18.382012
+ | url=https://ieeexplore.ieee.org/document/382012
+ }}
+* {{Citation
+ | last1=Willems
+ | last2=Shtarkov
+ | last3=Tjalkens
+ | year=1997
+ | title=Reflections on "The Context-Tree Weighting Method: Basic Properties"
+ | publication-place=IEEE Information Theory Society Newsletter
+ | volume=47
+ | number=1
+ | citeseerx=10.1.1.109.1872
+ }}
+* {{Citation
+ | last1=Begleiter
+ | last2=El-Yaniv
+ | last3=Yona
+ | year=2004
+ | title=On Prediction Using Variable Order Markov Models
+ | publication-place=Journal of Artificial Intelligence Research
+ | volume=22
+ | pages=385–421
+ | url=https://www.jair.org/index.php/jair/article/view/10394
+ | journal=[[Journal of Artificial Intelligence Research]]
+ | doi=10.1613/jair.1491
+ | s2cid=47180476
+ | arxiv=1107.0051
+ }}
 == External links ==
 * [http://www.data-compression.info/Algorithms/CTW/ Relevant CTW papers and implementations]
+* [https://web.archive.org/web/20150302190939/http://www.ele.tue.nl/ctw/ CTW Official Homepage]
+{{Compression methods}}
 [[Category:Lossless compression algorithms]]
-{{computer-stub}}
+{{comp-sci-stub}}