DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dongwoo | ko |
dc.contributor.author | Kim, Suin | ko |
dc.contributor.author | Oh, Alice Haeyun | ko |
dc.date.accessioned | 2015-06-03T06:09:07Z | - |
dc.date.available | 2015-06-03T06:09:07Z | - |
dc.date.created | 2015-05-27 | - |
dc.date.created | 2015-05-27 | - |
dc.date.created | 2015-05-27 | - |
dc.date.issued | 2012-06-26 | - |
dc.identifier.citation | 29th International Conference on Machine Learning, ICML 2012, pp.727 - 734 | - |
dc.identifier.uri | http://hdl.handle.net/10203/198678 | - |
dc.description.abstract | We describe a nonparametric topic model for labeled data. The model uses a mixture of random measures (MRM) as a base distribution of the Dirichlet process (DP) of the HDP framework, so we call it the DP-MRM. To model labeled data, we define a DP distributed random measure for each label, and the resulting model generates an unbounded number of topics for each label. We apply DP-MRM on single-labeled and multi-labeled corpora of documents and compare the performance on label prediction with MedLDA, LDA-SVM, and Labeled-LDA. We further enhance the model by incorporating ddCRP and modeling multi-labeled images for image segmentation and object labeling, comparing the performance with nCuts and rddCRP. | - |
dc.language | English | - |
dc.publisher | International Machine Learning Society (IMLS) | - |
dc.title | Dirichlet process with mixed random measures: a nonparametric topic model for labeled data | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-84867113166 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 727 | - |
dc.citation.endingpage | 734 | - |
dc.citation.publicationname | 29th International Conference on Machine Learning, ICML 2012 | - |
dc.identifier.conferencecountry | SC | - |
dc.identifier.conferencelocation | Edinburgh | - |
dc.contributor.localauthor | Oh, Alice Haeyun | - |
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