Adaptive context tree weighting

Alexander O'Neill; Marcus Hutter; Wen Shao; Peter Sunehag

doi:10.1109/DCC.2012.38

Adaptive context tree weighting

Alexander O'Neill^*, Marcus Hutter, Wen Shao, Peter Sunehag

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

We describe an adaptive context tree weighting (ACTW) algorithm, as an extension to the standard context tree weighting (CTW) algorithm. Unlike the standard CTW algorithm, which weights all observations equally regardless of the depth, ACTW gives increasing weight to more recent observations, aiming to improve performance in cases where the input sequence is from a non-stationary distribution. Data compression results show ACTW variants improving over CTW on merged files from standard compression benchmark tests while never being significantly worse on any individual file.

Original language	English
Title of host publication	Proceedings - DCC 2012
Subtitle of host publication	2012 Data Compression Conference
Pages	317-326
Number of pages	10
DOIs	https://doi.org/10.1109/DCC.2012.38
Publication status	Published - 2012
Event	2012 Data Compression Conference, DCC 2012 - Snowbird, UT, United States Duration: 10 Apr 2012 → 12 Apr 2012

Publication series

Name	Data Compression Conference Proceedings
ISSN (Print)	1068-0314

Conference

Conference	2012 Data Compression Conference, DCC 2012
Country/Territory	United States
City	Snowbird, UT
Period	10/04/12 → 12/04/12

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10.1109/DCC.2012.38

Cite this

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title = "Adaptive context tree weighting",

abstract = "We describe an adaptive context tree weighting (ACTW) algorithm, as an extension to the standard context tree weighting (CTW) algorithm. Unlike the standard CTW algorithm, which weights all observations equally regardless of the depth, ACTW gives increasing weight to more recent observations, aiming to improve performance in cases where the input sequence is from a non-stationary distribution. Data compression results show ACTW variants improving over CTW on merged files from standard compression benchmark tests while never being significantly worse on any individual file.",

author = "Alexander O'Neill and Marcus Hutter and Wen Shao and Peter Sunehag",

year = "2012",

doi = "10.1109/DCC.2012.38",

language = "English",

isbn = "9780769546568",

series = "Data Compression Conference Proceedings",

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note = "2012 Data Compression Conference, DCC 2012 ; Conference date: 10-04-2012 Through 12-04-2012",

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AU - O'Neill, Alexander

AU - Hutter, Marcus

AU - Shao, Wen

AU - Sunehag, Peter

PY - 2012

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AB - We describe an adaptive context tree weighting (ACTW) algorithm, as an extension to the standard context tree weighting (CTW) algorithm. Unlike the standard CTW algorithm, which weights all observations equally regardless of the depth, ACTW gives increasing weight to more recent observations, aiming to improve performance in cases where the input sequence is from a non-stationary distribution. Data compression results show ACTW variants improving over CTW on merged files from standard compression benchmark tests while never being significantly worse on any individual file.

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U2 - 10.1109/DCC.2012.38

DO - 10.1109/DCC.2012.38

M3 - Conference contribution

SN - 9780769546568

T3 - Data Compression Conference Proceedings

SP - 317

EP - 326

BT - Proceedings - DCC 2012

T2 - 2012 Data Compression Conference, DCC 2012

Y2 - 10 April 2012 through 12 April 2012

ER -

Adaptive context tree weighting

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