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dc.contributor.authorGyörfi, László
dc.contributor.authorWalk, Harro
dc.date.accessioned2017-10-25T07:20:51Z
dc.date.available2017-10-25T07:20:51Z
dc.date.issued2017-10-17
dc.identifier.urihttp://publications.mfo.de/handle/mfo/1314
dc.descriptionResearch in Pairs 2017en_US
dc.description.abstractFor a binary classification problem, the hypothesis testing is studied, that a component of the observation vector is not effective, i.e., that component carries no information for the classification. We introduce nearest neighbor and partitioning estimates of the Bayes error probability, which result in a strongly consistent test.en_US
dc.language.isoen_USen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesOberwolfach Preprints;2017,26
dc.subjectClassificationen_US
dc.subjectBayes Error Probabilityen_US
dc.subjectDimension Reductionen_US
dc.subjectStrongly Consistent Testen_US
dc.titleDetecting Ineffective Features for Pattern Recognitionen_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-2017-26
local.scientificprogramResearch in Pairs 2017en_US
local.series.idOWP-2017-26
local.subject.msc62
dc.identifier.urnurn:nbn:de:101:1-2017110614289
dc.identifier.ppn1657020541


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