Title:
Combining Classification and Clustering Tasks to Categorize Known and Unknown Classes

dc.contributor.advisor Kira, Zsolt
dc.contributor.author Shabbir, Javeria
dc.contributor.committeeMember Vela, Patricio A.
dc.contributor.committeeMember Hoffman, Judy
dc.contributor.department Computer Science
dc.date.accessioned 2023-01-10T16:25:03Z
dc.date.available 2023-01-10T16:25:03Z
dc.date.created 2022-12
dc.date.issued 2022-12-19
dc.date.submitted December 2022
dc.date.updated 2023-01-10T16:25:03Z
dc.description.abstract Since the past few years research has been directed towards the training of neural networks using unlabeled data or pairwise pseudo constraints known as unsupervised learning and semi-supervised learning respectively. In this thesis, we explored several methods to improve the performance of the current state-of-the-art algorithm for unsupervised learning called SCAN using semi-supervision from pairwise pseudo constraints.
dc.description.degree M.S.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/70166
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Unsupervised learning
dc.subject Semi-supervised learning
dc.subject SCAN
dc.title Combining Classification and Clustering Tasks to Categorize Known and Unknown Classes
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Kira, Zsolt
local.contributor.corporatename College of Computing
relation.isAdvisorOfPublication 7d182893-486b-4570-87b4-0c4ba0c10626
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
thesis.degree.level Masters
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