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0827b

Learning Theory and Approximation

dc.date.accessioned2019-10-24T13:43:45Z
dc.date.available2019-10-24T13:43:45Z
dc.date.issued2008
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3073
dc.description.abstractMathematical analysis of learning algorithms consists of bias measured by various kinds of approximation errors and variance investigated by probability and statistical analysis. This workshop has dealt with new developments and achievements from the past ten years, such as sparsity and dimension reduction for huge dimensional data, kernel learning and approximation by integral operators, or non-linear approximation and learning by scaling.
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-2008-30
local.series.idOWR-2008-30
local.subject.msc62
local.subject.msc41
local.subject.msc68
local.sortindex513
local.date-range29 Jun - 05 Jul 2008
local.workshopcode0827b
local.workshoptitleLearning Theory and Approximation
local.organizersKurt Jetter, Hohenheim; Steve Smale, Berkeley; Ding-Xuan Zhou, Hong Kong
local.report-nameWorkshop Report 2008,30
local.opc-photo-id0827b
local.publishers-doi10.4171/OWR/2008/30
local.ems-referenceJetter Kurt, Smale Steve, Zhou Ding-Xuan: Learning Theory and Approximation. Oberwolfach Rep. 5 (2008), 1655-1706. doi: 10.4171/OWR/2008/30


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