Detecting ineffective features for pattern recognition

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dc.bibliographicCitation.seriesTitleOberwolfach Preprints (OWP)eng
dc.bibliographicCitation.volume2017-26
dc.contributor.authorGyörfi, László
dc.contributor.authorWalk, Harro
dc.date.accessioned2017-11-24T21:32:57Z
dc.date.available2019-06-28T08:08:10Z
dc.date.issued2017
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.eng
dc.description.versionpublishedVersioneng
dc.formatapplication/pdf
dc.identifier.issn1864-7596
dc.identifier.urihttps://doi.org/10.34657/2207
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/2560
dc.language.isoengeng
dc.publisherOberwolfach : Mathematisches Forschungsinstitut Oberwolfacheng
dc.relation.doihttps://doi.org/10.14760/OWP-2017-26
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.eng
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.ger
dc.subject.ddc510eng
dc.subject.otherClassificationeng
dc.subject.otherBayes error probabilityeng
dc.subject.otherdimension reductioneng
dc.subject.otherstrongly consistent testeng
dc.titleDetecting ineffective features for pattern recognitioneng
dc.typeReporteng
dc.typeTexteng
tib.accessRightsopenAccesseng
wgl.contributorMFOeng
wgl.subjectMathematikeng
wgl.typeReport / Forschungsbericht / Arbeitspapiereng
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