The use of support vector machines in semi-supervised classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 배현주 | - |
dc.contributor.author | 김형우 | - |
dc.contributor.author | 신승준 | - |
dc.date.accessioned | 2022-04-12T15:42:39Z | - |
dc.date.available | 2022-04-12T15:42:39Z | - |
dc.date.created | 2022-04-12 | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/140141 | - |
dc.description.abstract | Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국통계학회 | - |
dc.title | The use of support vector machines in semi-supervised classification | - |
dc.title.alternative | The use of support vector machines in semi-supervised classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 신승준 | - |
dc.identifier.doi | 10.29220/CSAM.2022.29.2.193 | - |
dc.identifier.scopusid | 2-s2.0-85129388264 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.29, no.2, pp.193 - 202 | - |
dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 29 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 193 | - |
dc.citation.endPage | 202 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002823055 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | other | - |
dc.subject.keywordAuthor | dimension reduction | - |
dc.subject.keywordAuthor | $k$-means clustering | - |
dc.subject.keywordAuthor | semi-supervised classification | - |
dc.subject.keywordAuthor | support vector machines | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.