Stream-Based Active Learning with Multiple Kernels
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chae, Jeongmin | - |
dc.contributor.author | Hong, Songnam | - |
dc.date.accessioned | 2021-07-30T04:50:39Z | - |
dc.date.available | 2021-07-30T04:50:39Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2021-01 | - |
dc.identifier.issn | 1976-7684 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1636 | - |
dc.description.abstract | Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this paper we introduce a new research problem, named stream-based active multiple kernel learning (AMKL), where a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary in many real-world applications since acquiring a true label is costly or time-consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret \mathcal{O}(\sqrt{T}) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label-requests. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Stream-Based Active Learning with Multiple Kernels | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Songnam | - |
dc.identifier.doi | 10.1109/ICOIN50884.2021.9333940 | - |
dc.identifier.scopusid | 2-s2.0-85100716839 | - |
dc.identifier.bibliographicCitation | International Conference on Information Networking, v.2021, no.January, pp.718 - 722 | - |
dc.relation.isPartOf | International Conference on Information Networking | - |
dc.citation.title | International Conference on Information Networking | - |
dc.citation.volume | 2021 | - |
dc.citation.number | January | - |
dc.citation.startPage | 718 | - |
dc.citation.endPage | 722 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Active Learning | - |
dc.subject.keywordPlus | Curse of dimensionality | - |
dc.subject.keywordPlus | Multiple Kernel Learning | - |
dc.subject.keywordPlus | Multiple kernels | - |
dc.subject.keywordPlus | Nonlinear functions | - |
dc.subject.keywordPlus | Random features | - |
dc.subject.keywordPlus | Research problems | - |
dc.subject.keywordPlus | Selection criteria | - |
dc.subject.keywordPlus | Data streams | - |
dc.subject.keywordAuthor | Active learning | - |
dc.subject.keywordAuthor | multiple kernel learning | - |
dc.subject.keywordAuthor | online learning | - |
dc.subject.keywordAuthor | reproducing kernel Hilbert space | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9333940 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
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.