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Link-based Classification

dc.contributor.authorSen, Prithviraj
dc.contributor.authorGetoor, Lise
dc.date.accessioned2007-02-19T17:40:14Z
dc.date.available2007-02-19T17:40:14Z
dc.date.issued2007-02-19
dc.identifier.urihttp://hdl.handle.net/1903/4298
dc.description.abstractOver the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algorithms: loopy belief propagation, mean field relaxation labeling and iterative classification. We rate each algorithm in terms of its robustness to noise, both in attribute values and correlations across links. We also compare them across varying types of correlations across links.en
dc.format.extent602717 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.relation.ispartofseriesUM Computer Science Departmenten
dc.relation.ispartofseriesCS-TR-4858en
dc.relation.ispartofseriesUMIACSen
dc.relation.ispartofseriesUMIACS-TR-2007-11en
dc.titleLink-based Classificationen
dc.typeTechnical Reporten


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