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dc.contributor.authorMroueh, Youssef
dc.contributor.authorPoggio, Tomaso A.
dc.contributor.authorRosasco, Lorenzo Andrea
dc.contributor.authorSlotine, Jean-Jacques E.
dc.date.accessioned2014-12-16T15:43:57Z
dc.date.available2014-12-16T15:43:57Z
dc.date.issued2012-09
dc.identifier.issn1049-5258
dc.identifier.urihttp://hdl.handle.net/1721.1/92319
dc.description.abstractIn this paper we discuss a novel framework for multiclass learning, defined by a suitable coding/decoding strategy, namely the simplex coding, that allows us to generalize to multiple classes a relaxation approach commonly used in binary classification. In this framework, we develop a relaxation error analysis that avoids constraints on the considered hypotheses class. Moreover, using this setting we derive the first provably consistent regularized method with training/tuning complexity that is independent to the number of classes. We introduce tools from convex analysis that can be used beyond the scope of this paper.en_US
dc.language.isoen_US
dc.publisherNeural Information Processing Systems Foundationen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleMulticlass learning with simplex codingen_US
dc.typeArticleen_US
dc.identifier.citationMroueh, Youssef, Tomaso Poggio, Lorenzo Rosasco, and Jean-Jacques E. Slotine. "Multiclass learning with simplex coding." Advances in Neural Information Processing Systems 25 (2012).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Center for Biological & Computational Learningen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.mitauthorMroueh, Youssefen_US
dc.contributor.mitauthorPoggio, Tomaso A.en_US
dc.contributor.mitauthorRosasco, Lorenzo Andreaen_US
dc.contributor.mitauthorSlotine, Jean-Jacques E.en_US
dc.relation.journalAdvances in Neural Information Processing Systems (NIPS)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsMroueh, Youssef; Poggio, Tomaso; Rosasco, Lorenzo; Slotine, Jean-Jacques E.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-3944-0455
dc.identifier.orcidhttps://orcid.org/0000-0001-6376-4786
dc.identifier.orcidhttps://orcid.org/0000-0002-7161-7812
dc.identifier.orcidhttps://orcid.org/0000-0001-8798-1267
mit.licenseOPEN_ACCESS_POLICYen_US


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