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dc.contributor.authorKaraçalı, Bilge
dc.date.accessioned2016-12-22T12:04:15Z
dc.date.available2016-12-22T12:04:15Z
dc.date.issued2010-10
dc.identifier.citationKaraçalı, B. (2010). Quasi-supervised learning for biomedical data analysis. Pattern Recognition, 43(10), 3674-3682. doi:10.1016/j.patcog.2010.04.024en_US
dc.identifier.issn0031-3203
dc.identifier.urihttp://doi.org/10.1016/j.patcog.2010.04.024
dc.identifier.urihttp://hdl.handle.net/11147/2653
dc.description.abstractWe present a novel formulation for pattern recognition in biomedical data. We adopt a binary recognition scenario where a control dataset contains samples of one class only, while a mixed dataset contains an unlabeled collection of samples from both classes. The mixed dataset samples that belong to the second class are identified by estimating posterior probabilities of samples for being in the control or the mixed datasets. Experiments on synthetic data established a better detection performance against possible alternatives. The fitness of the method in biomedical data analysis was further demonstrated on real multi-color flow cytometry and multi-channel electroencephalography data. © 2010 Elsevier Ltd. All rights reserved.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.patcog.2010.04.024en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData flow analysisen_US
dc.subjectAbnormality detectionen_US
dc.subjectBiomedical data analysisen_US
dc.subjectElectroencephalographyen_US
dc.subjectSupport vector machinesen_US
dc.titleQuasi-supervised learning for biomedical data analysisen_US
dc.typearticleen_US
dc.contributor.authorIDTR11527en_US
dc.contributor.institutionauthorKaraçalı, Bilge
dc.relation.journalPattern Recognitionen_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume43en_US
dc.identifier.issue10en_US
dc.identifier.startpage3674en_US
dc.identifier.endpage3682en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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