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dc.contributor.authorCruz, Raúl
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2016-04-26T09:01:38Z
dc.date.available2016-04-26T09:01:38Z
dc.date.issued2007-01
dc.identifier.citationCruz, R., Vellido, A. "Elements of generative manifold learning for semi-supervised tasks". 2007.
dc.identifier.urihttp://hdl.handle.net/2117/86178
dc.description.abstractFor many real-world application problems, the availability of data labels for supervised learning is rather limited. It is often the case that a limited number of labelled cases is accompanied by a larger number of unlabeled ones. This is the setting for semi-supervised learning, in which unsupervised approaches assist the supervised problem and viceversa. In this report, we outline some basic theoretical foundations of semi-supervised learning using models of the generative manifold-learning family.
dc.format.extent15 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.otherSemi-supervised learning
dc.subject.otherManifold learning
dc.subject.otherGenerative methods
dc.titleElements of generative manifold learning for semi-supervised tasks
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.rights.accessOpen Access
local.identifier.drac1841845
dc.description.versionPostprint (published version)
local.citation.authorCruz, R.; Vellido, A.


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