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Properties of bagged nearest neighbour classifiers

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dc.contributor.authorHall, Peter
dc.contributor.authorSamworth, Richard
dc.date.accessioned2015-12-13T22:53:54Z
dc.date.issued2005
dc.date.updated2015-12-11T11:00:36Z
dc.description.abstractIt is shown that bagging, a computationally intensive method, asymptotically improves the performance of nearest neighbour classifiers provided that the resample size is less than 69% of the actual sample size, in the case of with-replacement bagging, or
dc.identifier.issn1369-7412
dc.identifier.urihttp://hdl.handle.net/1885/82020
dc.publisherAiden Press
dc.sourceJournal of the Royal Statistical Society Series B
dc.subjectKeywords: Bayes risk; Bootstrap; Classification error; Cross-validation; Density; Discrimination; Error rate; Marked point process; Poisson process; Prediction; Regret; Statistical learning; With-replacement sampling; Without-replacement sampling
dc.titleProperties of bagged nearest neighbour classifiers
dc.typeJournal article
local.bibliographicCitation.issue3
local.bibliographicCitation.lastpage379
local.bibliographicCitation.startpage363
local.contributor.affiliationHall, Peter, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationSamworth, Richard, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidHall, Peter, u7801145
local.contributor.authoruidSamworth, Richard, t475
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor010405 - Statistical Theory
local.identifier.ariespublicationMigratedxPub10320
local.identifier.citationvolume67
local.identifier.doi10.1111/j.1467-9868.2005.00506.x
local.identifier.scopusID2-s2.0-20744442569
local.type.statusPublished Version

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