Bayesian interpretation of adaptive fuzzy neural network model

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dc.contributor.authorLee, Sang Wanko
dc.contributor.authorKim, Dae-Jinko
dc.contributor.authorKim, Yong Sooko
dc.contributor.authorBien, Zeungnamko
dc.date.accessioned2018-07-24T02:34:13Z-
dc.date.available2018-07-24T02:34:13Z-
dc.date.created2018-07-11-
dc.date.created2018-07-11-
dc.date.issued2006-07-
dc.identifier.citation2006 IEEE International Conference on Fuzzy Systems, pp.2178 - 2183-
dc.identifier.urihttp://hdl.handle.net/10203/244259-
dc.description.abstractThis paper conveys Bayesian interpretation of improved Integrated Adaptive Fuzzy Clustering(IAFC), which is one of the adaptive Fuzzy Neural Network Models and suggests upper bound of vigilance parameter, which gives us a guideline to endow IAFC with flexibility within the framework of minimum risk classifier. Besides, we proposed the off-line and on-line learning strategy of IAFC. The proposed techniques are applied to construct facial expression recognition system dealing with neutral, happy, sad, and angry. We empirically show that proposed methods are able to outperform the conventional IAFC. © 2006 IEEE.-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleBayesian interpretation of adaptive fuzzy neural network model-
dc.typeConference-
dc.identifier.wosid000244063604034-
dc.type.rimsCONF-
dc.citation.beginningpage2178-
dc.citation.endingpage2183-
dc.citation.publicationname2006 IEEE International Conference on Fuzzy Systems-
dc.identifier.conferencecountryCN-
dc.identifier.conferencelocationVancouver, BC-
dc.identifier.doi10.1109/FUZZY.2006.1682002-
dc.contributor.localauthorLee, Sang Wan-
dc.contributor.nonIdAuthorKim, Dae-Jin-
dc.contributor.nonIdAuthorKim, Yong Soo-
dc.contributor.nonIdAuthorBien, Zeungnam-
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BC-Conference Papers(학술대회논문)
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