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dc.contributor.authorGyörfi, László
dc.contributor.authorDöring, Maik
dc.contributor.authorWalk, Harro
dc.date.accessioned2017-10-24T12:03:56Z
dc.date.available2017-10-24T12:03:56Z
dc.date.issued2017-10-16
dc.identifier.urihttp://publications.mfo.de/handle/mfo/1312
dc.descriptionResearch in Pairs 2017en_US
dc.description.abstractA binary classification problem is considered. The excess error probability of the k-nearest neighbor classification rule according to the error probability of the Bayes decision is revisited by a decomposition of the excess error probability into approximation and estimation error. Under a weak margin condition and under a modified Lipschitz condition, tight upper bounds are presented such that one avoids the condition that the feature vector is bounded.en_US
dc.language.isoen_USen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesOberwolfach Preprints;2017,25
dc.subjectRate of Convergenceen_US
dc.subjectClassificationen_US
dc.subjectError Probabilityen_US
dc.subjectK-Nearest Neighbor Ruleen_US
dc.titleExact Rate of Convergence of k-Nearest-Neighbor Classification Ruleen_US
dc.typePreprinten_US
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWP-2017-25
local.scientificprogramResearch in Pairs 2017en_US
local.series.idOWP-2017-25
local.subject.msc62
dc.identifier.urnurn:nbn:de:101:1-2017110614279
dc.identifier.ppn1657020495


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