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Learning Theory and Approximation

dc.date.accessioned2019-10-24T14:30:43Z
dc.date.available2019-10-24T14:30:43Z
dc.date.issued2012
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3302
dc.description.abstractLearning theory studies data structures from samples and aims at understanding unknown function relations behind them. This leads to interesting theoretical problems which can be often attacked with methods from Approximation Theory. This workshop - the second one of this type at the MFO - has concentrated on the following recent topics: Learning of manifolds and the geometry of data; sparsity and dimension reduction; error analysis and algorithmic aspects, including kernel based methods for regression and classification; application of multiscale aspects and of refinement algorithms to learning.
dc.titleLearning Theory and Approximation
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWR-2012-31
local.series.idOWR-2012-31
local.subject.msc41
local.subject.msc62
local.subject.msc68
local.sortindex742
local.date-range24 Jun - 30 Jun 2012
local.workshopcode1226
local.workshoptitleLearning Theory and Approximation
local.organizersKurt Jetter, Hohenheim; Steve Smale, Hong Kong; Ding-Xuan Zhou, Hong Kong
local.report-nameWorkshop Report 2012,31
local.opc-photo-id1226
local.publishers-doi10.4171/OWR/2012/31
local.ems-referenceJetter Kurt, Smale Steve, Zhou Ding-Xuan: Learning Theory and Approximation. Oberwolfach Rep. 9 (2012), 1895-1948. doi: 10.4171/OWR/2012/31


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