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dc.contributor.authorFaaeq, Ainuddin
dc.contributor.authorGürüler, Hüseyin
dc.contributor.authorPeker, Musa
dc.date.accessioned2020-11-20T14:51:24Z
dc.date.available2020-11-20T14:51:24Z
dc.date.issued2018
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12809/1742
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) - MAY 02-05, 2018 - Izmir, TURKEYen_US
dc.descriptionWOS: 000511448500294en_US
dc.description.abstractThis paper presents fast categorization or classification of images on an animal dataset using different classification algorithm in combination with manifold learning algorithms. The paper will focus on comparing the effects of different non-linear dimensionality reduction algorithms on speed and accuracy of different classification algorithms. It examines how manifold learning algorithms can improve classification speed by reducing the number of features in the vector representation of images while keeping the classification accuracy high.en_US
dc.description.sponsorshipIEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univen_US
dc.item-language.isoengen_US
dc.publisherIeeeen_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.item-rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectImage Classificationen_US
dc.subjectManifold Learningen_US
dc.subjectDimensionality Reductionen_US
dc.subjectImage Miningen_US
dc.subjectMachine Learningen_US
dc.titleImage Classification using Manifold Learning Based Non-Linear Dimensionality Reductionen_US
dc.item-typeconferenceObjecten_US
dc.contributor.departmentMÜ, Teknoloji Fakültesi, Bilişim Sistemleri Mühendisliği Bölümüen_US
dc.contributor.institutionauthorFaaeq, Ainuddin
dc.contributor.institutionauthorGürüler, Hüseyin
dc.contributor.institutionauthorPeker, Musa
dc.relation.journal2018 26Th Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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