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Adaptive context tree weighting

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

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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...[Show more]

dc.contributor.authorO'Neill, Alexander
dc.contributor.authorHutter, Marcus
dc.contributor.authorShao, Wen
dc.contributor.authorSunehag, Peter
dc.date.accessioned2015-08-19T05:48:14Z
dc.date.available2015-08-19T05:48:14Z
dc.identifier.isbn978-1-4673-0715-4
dc.identifier.issn1068-0314
dc.identifier.urihttp://hdl.handle.net/1885/14804
dc.description.abstractWe 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.
dc.publisherIEEE
dc.relation.ispartof2012 Data Compression Conference (DCC), 10-12 April 2012, Snowbird, UT
dc.rightsAuthors are free to post the accepted version of their articles on their personal Web sites or those of their employers. http://www.ieee.org/publications_standards/publications/rights/index.html as at 19/08/2015
dc.rights© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.titleAdaptive context tree weighting
dc.typeConference paper
dc.date.issued2012
local.type.statusAccepted Version
local.contributor.affiliationO'Neill, A., Research School of Computer Science, The Australian National University
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.contributor.affiliationShao, W., Research School of Computer Science, The Australian National University
local.contributor.affiliationSunehag, P., Research School of Computer Science, The Australian National University
dc.relationhttp://purl.org/au-research/grants/arc/DP0988049
local.bibliographicCitation.startpage317
local.bibliographicCitation.lastpage326
local.identifier.doi10.1109/DCC.2012.38
CollectionsANU Research Publications

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