Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91840
DC FieldValueLanguage
dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorChen, Xen_US
dc.creatorTang, Ben_US
dc.creatorFan, Jen_US
dc.creatorGuo, Xen_US
dc.date.accessioned2021-12-23T02:14:45Z-
dc.date.available2021-12-23T02:14:45Z-
dc.identifier.issn0885-064Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/91840-
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.subjectLearning theoryen_US
dc.subjectOnline learningen_US
dc.subjectGradient descenten_US
dc.subjectReproducing kernel Hilbert spaceen_US
dc.subjectError analysisen_US
dc.titleOnline gradient descent algorithms for functional data learningen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume70en_US
dc.identifier.doi10.1016/j.jco.2021.101635en_US
dcterms.abstractFunctional linear model is a fruitfully applied general framework for regression problems, including those with intrinsically infinite-dimensional data. Online gradient descent methods, despite their evidenced power of processing online or large-sized data, are not well studied for learning with functional data. In this paper, we study reproducing kernel-based online learning algorithms for functional data, and derive convergence rates for the expected excess prediction risk under both online and finite-horizon settings of step-sizes respectively. It is well understood that nontrivial uniform convergence rates for the estimation task depend on the regularity of the slope function. Surprisingly, the convergence rates we derive for the prediction task can assume no regularity from slope. Our analysis reveals the intrinsic difference between the estimation task and the prediction task in functional data learning.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationJournal of complexity, June 2022, v. 70, 101635en_US
dcterms.isPartOfJournal of complexityen_US
dcterms.issued2022-06-
dc.identifier.eissn1090-2708en_US
dc.identifier.artn101635en_US
dc.description.validate202112 bcvcen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1122-n01-
dc.identifier.SubFormID43965-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRGC: 15304917en_US
dc.description.fundingTextOthers: ZE8Qen_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2024-06-30en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2024-06-30
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