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Measuring statistical dependence with Hilbert-Schmidt norms

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dc.contributor.authorGretton, Arthur
dc.contributor.authorBousquet, Olivier
dc.contributor.authorSmola, Alexander
dc.contributor.authorSchoelkopf, Bernhard
dc.coverage.spatialSingapore
dc.date.accessioned2015-12-13T22:55:00Z
dc.date.createdOctober 8-11 2005
dc.date.issued2005
dc.date.updated2015-12-11T11:07:34Z
dc.identifier.isbn354029242X
dc.identifier.urihttp://hdl.handle.net/1885/82348
dc.publisherSpringer
dc.relation.ispartofseriesInternational Conference on Algorithmic Learning Theory (ALT 2005)
dc.sourceAlgorithmic Learning Theory: Proceedings of the 16th International Conference on Algorithmic Learning Theory (ALT-05) - LNAI 3734
dc.source.urihttp://www.springerlink.com/content/4l3kjpj6kjw7
dc.titleMeasuring statistical dependence with Hilbert-Schmidt norms
dc.typeConference paper
local.bibliographicCitation.lastpage77
local.bibliographicCitation.startpage63
local.contributor.affiliationGretton, Arthur, Max Planck Institute for Biological Cybernetics
local.contributor.affiliationBousquet, Olivier, Pertinence
local.contributor.affiliationSmola, Alexander, College of Engineering and Computer Science, ANU
local.contributor.affiliationSchoelkopf, Bernhard, Max Planck Institute for Biological Cybernetics
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.ariespublicationMigratedxPub10605
local.type.statusPublished Version

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