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Conference Paper: Gaussian process machine learning based ITO algorithm

TitleGaussian process machine learning based ITO algorithm
Authors
Ma, ChuangYang, YongjianDu, ZhanweiZhang, Chijun
KeywordsGaussian process
fluctuation ratio
incremental inheritance
ITO
category theory
Issue Date2014
Citation
Proceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014, 2014, p. 38-41 How to Cite?
DOI: http://dx.doi.org/10.1109/BWCCA.2014.43
Abstract© 2014 IEEE. Taking the Gaussian process (GP) regression model as ITO's fluctuation operator, we propose a new mixed algorithm called GITO in order to overcome the local minima problem. Through learning the particles' mobility models, ITO's capacity of local searching and global searching is strengthened. Meanwhile, we give the proof procedure to verify ITO's fluctuation operator and GP are logically equivalent. Finally, the experiments show GITO's better convergence rate and performance.
Persistent Identifierhttp://hdl.handle.net/10722/296136
ISI Accession Number ID
WOS:000380452800007

 

DC FieldValueLanguage
dc.contributor.authorMa, Chuang-
dc.contributor.authorYang, Yongjian-
dc.contributor.authorDu, Zhanwei-
dc.contributor.authorZhang, Chijun-
dc.date.accessioned2021-02-11T04:52:55Z-
dc.date.available2021-02-11T04:52:55Z-
dc.date.issued2014-
dc.identifier.citationProceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014, 2014, p. 38-41-
dc.identifier.urihttp://hdl.handle.net/10722/296136-
dc.description.abstract© 2014 IEEE. Taking the Gaussian process (GP) regression model as ITO's fluctuation operator, we propose a new mixed algorithm called GITO in order to overcome the local minima problem. Through learning the particles' mobility models, ITO's capacity of local searching and global searching is strengthened. Meanwhile, we give the proof procedure to verify ITO's fluctuation operator and GP are logically equivalent. Finally, the experiments show GITO's better convergence rate and performance.-
dc.languageeng-
dc.relation.ispartofProceedings - 2014 9th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2014-
dc.subjectGaussian process-
dc.subjectfluctuation ratio-
dc.subjectincremental inheritance-
dc.subjectITO-
dc.subjectcategory theory-
dc.titleGaussian process machine learning based ITO algorithm-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/BWCCA.2014.43-
dc.identifier.scopuseid_2-s2.0-84988288879-
dc.identifier.spage38-
dc.identifier.epage41-
dc.identifier.isiWOS:000380452800007-

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