Adaptive Online Prediction by Following the Perturbed Leader

dc.contributor.authorHutter, Marcus
dc.contributor.authorPoland, Jan
dc.date.accessioned2015-12-10T22:41:19Z
dc.date.issued2005
dc.date.updated2016-02-24T11:44:54Z
dc.description.abstractWhen applying aggregating strategies to Prediction with Expert Advice (PEA), the learning rate must be adaptively tuned. The natural choice of √complexity/current loss renders the analysis of Weighted Majority (WM) derivatives quite complicated. In part
dc.identifier.issn1532-4435
dc.identifier.urihttp://hdl.handle.net/1885/57847
dc.publisherMIT Press
dc.rightsCopyright Information: © 2005 Marcus Hutter and Jan Poland. http://www.sherpa.ac.uk/romeo/issn/1532-4435/..."Publisher's version/PDF may be used. On open access repositories" from SHERPA/RoMEO site (as at 1/09/15).
dc.sourceJournal of Machine Learning Research
dc.source.urihttp://jmlr.csail.mit.edu/papers/volume6/hutter05a/hutter05a.pdf
dc.subjectKeywords: Algorithms; Boundary value problems; Expert systems; Hierarchical systems; Learning systems; Online systems; Perturbation techniques; Probability; Adaptive adversary; Adaptive learning rate; Expected and high probability bounds; Follow the perturbed leade Adaptive adversary; Adaptive learning rate; Expected and high probability bounds; Follow the perturbed leader; General alphabet and loss; General weights; Hierarchy of experts; Online sequential prediction; Prediction with expert advice
dc.titleAdaptive Online Prediction by Following the Perturbed Leader
dc.typeJournal article
dcterms.accessRightsOpen Access
local.bibliographicCitation.issueApril
local.bibliographicCitation.lastpage660
local.bibliographicCitation.startpage639
local.contributor.affiliationHutter, Marcus, College of Engineering and Computer Science, ANU
local.contributor.affiliationPoland, Jan, IDSIA-Istituto Dalle Molle di Studi sull Intelligenza Artificiale
local.contributor.authoremailu4350841@anu.edu.au
local.contributor.authoruidHutter, Marcus, u4350841
local.description.notesImported from ARIES
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu8803936xPUB417
local.identifier.citationvolume6
local.identifier.scopusID2-s2.0-21844465698
local.identifier.uidSubmittedByu8803936
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
01_Hutter_Adaptive_Online_Prediction_by_2005.pdf
Size:
329.61 KB
Format:
Adobe Portable Document Format
Back to topicon-arrow-up-solid
 
APRU
IARU
 
edX
Group of Eight Member

Acknowledgement of Country

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.


Contact ANUCopyrightDisclaimerPrivacyFreedom of Information

+61 2 6125 5111 The Australian National University, Canberra

TEQSA Provider ID: PRV12002 (Australian University) CRICOS Provider Code: 00120C ABN: 52 234 063 906