Active Learning With Multiple Kernels
DC Field | Value | Language |
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dc.contributor.author | Hong, Song nam | - |
dc.contributor.author | Chae, Jeongmin | - |
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 | 2162-237X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1635 | - |
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 article, we introduce a new research problem, named stream-based active MKL (AMKL), in which a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary for many real-world applications as acquiring a true label is costly or time consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret O(√T) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label requests. Furthermore, we present AMKL with an adaptive kernel selection (named AMKL-AKS) in which irrelevant kernels can be excluded from a kernel dictionary ``on the fly.'' This approach improves the efficiency of active learning and the accuracy of function learning. Via numerical tests with real data sets, we verify the superiority of AMKL-AKS, yielding a similar accuracy performance with OMKL counterpart using a fewer number of labeled data. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Active Learning With Multiple Kernels | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Song nam | - |
dc.identifier.doi | 10.1109/TNNLS.2020.3047953 | - |
dc.identifier.scopusid | 2-s2.0-85099730698 | - |
dc.identifier.wosid | 000733451400001 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Neural Networks and Learning Systems, v.33, no.7, pp.2980 - 2994 | - |
dc.relation.isPartOf | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.citation.title | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.citation.volume | 33 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 2980 | - |
dc.citation.endPage | 2994 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | ONLINE | - |
dc.subject.keywordAuthor | Active learning (AL) | - |
dc.subject.keywordAuthor | Biomedical imaging | - |
dc.subject.keywordAuthor | Dictionaries | - |
dc.subject.keywordAuthor | Kernel | - |
dc.subject.keywordAuthor | Labeling | - |
dc.subject.keywordAuthor | multiple kernel learning (MKL) | - |
dc.subject.keywordAuthor | online learning | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | Radio frequency | - |
dc.subject.keywordAuthor | reproducing kernel Hilbert space (RKHS). | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9325069 | - |
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