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Improved Anomaly Scoring for Anomaly Detection Using Auto Encoder based Unsupervised Learning from Unlabeled Data

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dc.contributor.authorJun-Geol Baek-
dc.date.accessioned2021-09-14T17:37:37Z-
dc.date.available2021-09-14T17:37:37Z-
dc.date.created2021-09-14-
dc.date.issued2021-07-12-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/126799-
dc.publisherEURO-
dc.titleImproved Anomaly Scoring for Anomaly Detection Using Auto Encoder based Unsupervised Learning from Unlabeled Data-
dc.title.alternativeImproved Anomaly Scoring for Anomaly Detection Using Auto Encoder based Unsupervised Learning from Unlabeled Data-
dc.typeConference-
dc.contributor.affiliatedAuthorJun-Geol Baek-
dc.identifier.bibliographicCitation31st European Conference on Operational Research (EURO 2021)-
dc.relation.isPartOf31st European Conference on Operational Research (EURO 2021)-
dc.relation.isPartOfEURO 2021-
dc.citation.title31st European Conference on Operational Research (EURO 2021)-
dc.citation.conferencePlaceGR-
dc.citation.conferencePlaceAthens, Greece-
dc.citation.conferenceDate2021-07-11-
dc.type.rimsCONF-
dc.description.journalClass1-
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