Elements of generative manifold learning for semi-supervised tasks

All of RECERCAT

To access the full text documents, please follow this link: http://hdl.handle.net/2117/86178

dc.contributor	Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor	Universitat Politècnica de Catalunya. SOCO - Soft Computing
dc.contributor.author	Cruz, Raúl
dc.contributor.author	Vellido Alcacena, Alfredo
dc.date	2007-01
dc.identifier.citation	Cruz, R., Vellido, A. "Elements of generative manifold learning for semi-supervised tasks". 2007.
dc.identifier.uri	http://hdl.handle.net/2117/86178
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject	Semi-supervised learning
dc.subject	Manifold learning
dc.subject	Generative methods
dc.title	Elements of generative manifold learning for semi-supervised tasks
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/report
dc.description.abstract	For 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.