Autonomic machine learning platform
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Keon Myung | - |
dc.contributor.author | Yoo, Jaesoo | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Lee, Jee-Hyong | - |
dc.contributor.author | Hong, Jiman | - |
dc.date.accessioned | 2022-07-08T20:26:23Z | - |
dc.date.available | 2022-07-08T20:26:23Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 0268-4012 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146648 | - |
dc.description.abstract | Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning. In this paper, we propose an autonomic machine learning platform which provides the decision factors to be made during the developing of machine learning applications. In the proposed autonomic machine learning platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic machine learning platform can be used to derive a high-quality learning result by minimizing experts' interventions and reducing the number of design selections that require expert knowledge and intuition. We also demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically require considerable amounts of security sensitive information. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Autonomic machine learning platform | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1016/j.ijinfomgt.2019.07.003 | - |
dc.identifier.scopusid | 2-s2.0-85068552680 | - |
dc.identifier.wosid | 000489702000038 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, v.49, pp.491 - 501 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT | - |
dc.citation.title | INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT | - |
dc.citation.volume | 49 | - |
dc.citation.startPage | 491 | - |
dc.citation.endPage | 501 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Information Science & Library Science | - |
dc.relation.journalWebOfScienceCategory | Information Science & Library Science | - |
dc.subject.keywordPlus | E-learning | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Smart city | - |
dc.subject.keywordPlus | Accurate prediction | - |
dc.subject.keywordPlus | Autonomic level | - |
dc.subject.keywordPlus | Decision factors | - |
dc.subject.keywordPlus | Design selections | - |
dc.subject.keywordPlus | Expert knowledge | - |
dc.subject.keywordPlus | Industrial fields | - |
dc.subject.keywordPlus | Machine learning applications | - |
dc.subject.keywordPlus | Sensitive informations | - |
dc.subject.keywordPlus | Machine learning | - |
dc.subject.keywordAuthor | Autonomic machine learning platform | - |
dc.subject.keywordAuthor | Autonomic level | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Smart City | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S026840121831154X?via%3Dihub | - |
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