DC FieldValueLanguage
dc.contributor.authorJelisavčić, Vladisaven
dc.contributor.authorFurlan, Bojanen
dc.contributor.authorProtić, Jelicaen
dc.contributor.authorMilutinović, Veljkoen
dc.date.accessioned2020-05-01T20:12:32Z-
dc.date.available2020-05-01T20:12:32Z-
dc.date.issued2012-08-22en
dc.identifier.isbn978-9-532-33072-4en
dc.description.abstractSurvey of probabilistc topic models is presented with emphasis on fundamentally different approaches used in modeling. Introduced classification differs from earlier efforts, providing a complementary view of the field. Purpose of this survey is to provide a brief overview of the current probailistic topic models as well as an inspiration for future research.en
dc.publisherMIPRO-
dc.relation.ispartofMIPRO 2012 - 35th International Convention on Information and Communication Technology, Electronics and Microelectronics - Proceedingsen
dc.titleTopic models and advanced algorithms for profiling of knowledge in scientific papersen
dc.typeConference Paperen
dc.relation.conference35th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2012; Opatija; Croatia; 21 May 2012 through 25 May 2012-
dc.identifier.scopus2-s2.0-84865066465en
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage1030en
dc.relation.lastpage1035en
item.fulltextNo Fulltext-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
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