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Comparison of Machine Learning Methods for the Sequence Labelling Applications

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dc.contributor.authorAmasyali, Mehmet Fatih
dc.contributor.authorBilgin, Metin
dc.date.accessioned2026-06-27T13:53:56Z
dc.date.issued2015
dc.description.abstractIn this study, on artificial data sets, it was compared condition random fields(CRF) and classical machine learning(CML) types. First part of this study, the performances of CRF and CML types were measured on artificial data sets. As the result of studies, CML types, except Naive Bayes, performanced higher than CRF. The success of NR and CRF is high when the outputs consist of one distribution, in other case it stays low. Besides in this study, it was evaluated the effect of education set size on success. The second study was made to test this situation.en
dc.identifier.endpage506
dc.identifier.isbn978-1-4673-7386-9
dc.identifier.issn2165-0608
dc.identifier.startpage503
dc.identifier.urihttps://hdl.handle.net/20.500.14981/55441
dc.identifier.wos000380500900104
dc.language.isotur
dc.publisherIEEE
dc.relation.conference23nd Signal Processing and Communications Applications Conference (SIU)
dc.relation.ispartof2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
dc.subjectConditional Random Fields
dc.subjectSequence Labeling
dc.subjectEngineering
dc.subjectTelecommunications
dc.titleComparison of Machine Learning Methods for the Sequence Labelling Applications
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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