Publication:

Optimised one-class classification performance

Date

2022
Journal article
https://doi.org/10.1007/s10994-022-06147-2
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dc.contributor.authorLenz, Oliver Urs
dc.contributor.authorPeralta, Daniel
dc.contributor.authorCornelis, Chris
dc.contributor.imecauthorPeralta, Daniel
dc.contributor.orcidimecPeralta, Daniel::0000-0002-7544-8411
dc.date.accessioned2022-08-29T09:02:03Z
dc.date.available2022-03-26T02:08:25Z
dc.date.available2022-05-19T08:41:09Z
dc.date.available2022-08-29T09:02:03Z
dc.date.embargo2023-05-07
dc.date.issued2022
dc.description.wosFundingTextThe research reported in this paper was conducted with the financial support of the Odysseus programme of the Research Foundation -Flanders (FWO). D. Peralta is a Postdoctoral Fellow of the Research Foundation -Flanders (FWO, 170303/12X1619N).
dc.identifier.doi10.1007/s10994-022-06147-2
dc.identifier.issn0885-6125
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/39533
dc.publisherSPRINGER
dc.source.beginpage2863
dc.source.endpage2883
dc.source.issue8
dc.source.journalMACHINE LEARNING
dc.source.numberofpages21
dc.source.volume111
dc.subject.disciplineComputer science/information technology
dc.subject.keywordsData descriptors
dc.subject.keywordsHyperparameter optimisation
dc.subject.keywordsNovelty detection
dc.subject.keywordsOne-class classification
dc.subject.keywordsSemi-supervised outlier detection
dc.title

Optimised one-class classification performance

dc.typeJournal article
dspace.entity.typePublication
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