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