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A Short Introduction to Learning with Kernels

dc.contributor.authorSchoelkopf, Bernhard
dc.contributor.authorSmola, Alexander
dc.date.accessioned2015-12-13T23:07:13Z
dc.date.issued2003
dc.date.updated2015-12-12T08:08:18Z
dc.description.abstractWe briefly describe the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the ν-trick, various kernels a
dc.identifier.isbn3540005293
dc.identifier.urihttp://hdl.handle.net/1885/86108
dc.publisherSpringer
dc.relation.ispartofAdvanced Lectures on Machine Learning
dc.relation.isversionof1 Edition
dc.titleA Short Introduction to Learning with Kernels
dc.typeBook chapter
local.bibliographicCitation.lastpage64
local.bibliographicCitation.placeofpublicationGermany
local.bibliographicCitation.startpage41
local.contributor.affiliationSchoelkopf, Bernhard, Max Planck Institute for Biological Cybernetics
local.contributor.affiliationSmola, Alexander, College of Engineering and Computer Science, ANU
local.contributor.authoruidSmola, Alexander, u4039398
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub14865
local.identifier.scopusID2-s2.0-35248821683
local.type.statusPublished Version

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