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Kernel methods in machine learning

Hofmann, Thomas; Schölkopf, Bernhard; Smola, Alexander J.

Description

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions...[Show more]

dc.contributor.authorHofmann, Thomas
dc.contributor.authorSchölkopf, Bernhard
dc.contributor.authorSmola, Alexander J.
dc.date.accessioned2016-03-04T01:10:21Z
dc.date.available2016-03-04T01:10:21Z
dc.identifier.issn0090-5364
dc.identifier.urihttp://hdl.handle.net/1885/100161
dc.description.abstractWe review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.
dc.publisherInstitute of Mathematical Statistics
dc.rights© Institute of Mathematical Statistics, 2008. http://www.sherpa.ac.uk/romeo/issn/0090-5364..."author can archive publisher's version/PDF. On author's personal website or open access repository" from SHERPA/RoMEO site (as at 4/03/16).
dc.sourceAnnals of Statistics
dc.subjectKeywords: Graphical models; Machine learning; Reproducing kernels; Support vector machines
dc.titleKernel methods in machine learning
dc.typeJournal article
local.description.notesImported from ARIES. At the time of publication the author Smola was affiliated with National ICT Australia.
local.identifier.citationvolume36
dc.date.issued2008
local.identifier.absfor080109
local.identifier.ariespublicationu8803936xPUB326
local.publisher.urlhttp://imstat.org/en/index.html
local.type.statusPublished Version
local.contributor.affiliationHofmann, Thomas, Brown University, United States of America
local.contributor.affiliationSchoelkopf, Bernhard, Max Planck Institute for Biological Cybernetics, Germany
local.contributor.affiliationSmola, Alexander, College of Engineering and Computer Science, College of Engineering and Computer Science, Research School of Computer Science, The Australian National University
local.bibliographicCitation.issue3
local.bibliographicCitation.startpage1171
local.bibliographicCitation.lastpage1220
local.identifier.doi10.1214/009053607000000677
dc.date.updated2016-06-14T09:18:25Z
local.identifier.scopusID2-s2.0-51049096780
dcterms.accessRightsOpen Access
CollectionsANU Research Publications

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