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Online Learning as Stochastic Approximation of Regularization Paths: Optimality and Almost-Sure Convergence

contributor authorTarres, Pierre
contributor authorYuan Yao
date accessioned2020-03-13T00:10:46Z
date available2020-03-13T00:10:46Z
date issued2014
identifier issn0018-9448
identifier other6842642.pdf
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/1137256?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titleOnline Learning as Stochastic Approximation of Regularization Paths: Optimality and Almost-Sure Convergence
typeJournal Paper
contenttypeMetadata Only
identifier padid8319114
subject keywordsHilbert spaces
subject keywordsapproximation theory
subject keywordslearning (artificial intelligence)
subject keywordsminimax techniques
subject keywordsregression analysis
subject keywordsstochastic processes
subject keywordsBernstein-type inequalities
subject keywordsRKHS
subject keywordsalmost sure convergence
subject keywordsbatch learning setting
subject keywordsmean square distance
subject keywordsminimax
subject keywordsonline learning algorithm
subject keywordsoptimality convergence
subject keywordsregression function
subject keywordsregularization paths
subject keywordsreproducing kernel Hilbert spaces
subject keywordsstochastic approximation
subject keywordsApproximation methods
subject keywordsConvergence
subject keywordsEducational institutions
subject keywordsHilbert space
subject keywordsKernel
subject keywordsProbabil
identifier doi10.1109/TIT.2014.2332531
journal titleInformation Theory, IEEE Transactions on
journal volume60
journal issue9
filesize403231
citations0


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