| dc.contributor.author | Györfi, László | |
| dc.contributor.author | Walk, Harro | |
| dc.date.accessioned | 2017-10-25T07:20:51Z | |
| dc.date.available | 2017-10-25T07:20:51Z | |
| dc.date.issued | 2017-10-17 | |
| dc.identifier.uri | http://publications.mfo.de/handle/mfo/1314 | |
| dc.description | Research in Pairs 2017 | en_US |
| dc.description.abstract | For 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.iso | en_US | en_US |
| dc.publisher | Mathematisches Forschungsinstitut Oberwolfach | en_US |
| dc.relation.ispartofseries | Oberwolfach Preprints;2017,26 | |
| dc.subject | Classification | en_US |
| dc.subject | Bayes Error Probability | en_US |
| dc.subject | Dimension Reduction | en_US |
| dc.subject | Strongly Consistent Test | en_US |
| dc.title | Detecting Ineffective Features for Pattern Recognition | en_US |
| dc.type | Preprint | en_US |
| dc.rights.license | Dieses 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.license | This 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.doi | 10.14760/OWP-2017-26 | |
| local.scientificprogram | Research in Pairs 2017 | en_US |
| local.series.id | OWP-2017-26 | |
| local.subject.msc | 62 | |
| dc.identifier.urn | urn:nbn:de:101:1-2017110614289 | |
| dc.identifier.ppn | 1657020541 | |
