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dc.contributor.authorCataltepe, Zehra
dc.contributor.authorSonmez, Abdullah
dc.contributor.authorBax, Eric
dc.contributor.authorLi, James
dc.date.accessioned2021-03-02T22:53:32Z
dc.date.available2021-03-02T22:53:32Z
dc.identifier.citationLi J., Sonmez A., Cataltepe Z., Bax E., "Validation of Network Classifiers", Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition (SSPR) / International Workshop on Statistical Techniques in Pattern Recognition (SPR), Hiroshima, Japonya, 7 - 09 Kasım 2012, cilt.7626, ss.448-457
dc.identifier.othervv_1032021
dc.identifier.otherav_0f4844f9-758c-4b9d-b02a-5afdf2a4248f
dc.identifier.urihttp://hdl.handle.net/20.500.12627/15812
dc.identifier.urihttps://doi.org/10.1007/978-3-642-34166-3_49
dc.description.abstractThis paper develops PAC (probably approximately correct) error bounds for network classifiers in the transductive setting, where the network node inputs and links are all known, the training nodes class labels are known, and the goal is to classify a working set of nodes that have unknown class labels. The bounds are valid for any model of network generation. They require working nodes to be selected independently, but not uniformly at random. For example, they allow different regions of the network to have different densities of unlabeled nodes.
dc.language.isoeng
dc.subjectBiyoenformatik
dc.subjectMühendislik ve Teknoloji
dc.subjectBilgisayar Bilimleri
dc.subjectAlgoritmalar
dc.subjectBİLGİSAYAR BİLİMİ, TEORİ VE YÖNTEM
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectBilgisayar Bilimi
dc.subjectBİLGİSAYAR BİLİMİ, YAPAY ZEKA
dc.titleValidation of Network Classifiers
dc.typeBildiri
dc.contributor.departmentCornell University , ,
dc.identifier.volume7626
dc.contributor.firstauthorID139087


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