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New Support Vector Algorithms

dc.contributor.authorSchoelkopf, Bernhard
dc.contributor.authorSmola, Alexander
dc.contributor.authorWilliamson, Robert
dc.contributor.authorBartlett, Peter
dc.date.accessioned2015-12-13T23:15:38Z
dc.date.available2015-12-13T23:15:38Z
dc.date.issued2000
dc.date.updated2015-12-12T08:44:17Z
dc.description.abstractWe propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the
dc.identifier.issn0899-7667
dc.identifier.urihttp://hdl.handle.net/1885/88973
dc.publisherMIT Press
dc.sourceNeural Computation
dc.titleNew Support Vector Algorithms
dc.typeJournal article
local.bibliographicCitation.issue5
local.bibliographicCitation.lastpage1245
local.bibliographicCitation.startpage1207
local.contributor.affiliationSchoelkopf, Bernhard, Max Planck Institute for Biological Cybernetics
local.contributor.affiliationSmola, Alexander, College of Engineering and Computer Science, ANU
local.contributor.affiliationWilliamson, Robert, College of Engineering and Computer Science, ANU
local.contributor.affiliationBartlett, Peter, College of Engineering and Computer Science, ANU
local.contributor.authoruidSmola, Alexander, u4039398
local.contributor.authoruidWilliamson, Robert, u9000163
local.contributor.authoruidBartlett, Peter, u9301805
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub18847
local.identifier.citationvolume12
local.identifier.scopusID2-s2.0-17444438778
local.type.statusPublished Version

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