Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44332
DC FieldValueLanguage
dc.contributor.authorJuang, C.F.en_US
dc.contributor.author莊家峰zh_TW
dc.contributor.authorChiu, S.H.en_US
dc.contributor.authorChang, S.W.en_US
dc.date2007zh_TW
dc.date.accessioned2014-06-06T08:12:09Z-
dc.date.available2014-06-06T08:12:09Z-
dc.identifier.issn1063-6706zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44332-
dc.description.abstractA self-organizing Takagi-Sugeno (TS)-type fuzzy network with support vector learning (SOTFN-SV) is proposed in this paper. The proposed SOTFN-SV is inspired by analysis of TS-type fuzzy systems and composite-kernel support vector machine (SVM). SOTFN-SV is a fuzzy system constructed by the hybridization of fuzzy clustering and SVM. The antecedent part of SOTFN-SV is generated via fuzzy clustering of the input data, and then SVM is used to tune the consequent part parameters to give the network better generalization performance. For demonstration, SOTFN-SV is applied to several classification problems, especially the skin color classification problem. In the skin color classification application, each color pixel is represented by hue and saturation (HS) color space. To represent color information by histogram as accurately as possible, a nonuniform partition of HS space is proposed. For comparison, SVMs and other fuzzy systems trained by SVM or neural networks are applied to the same classification problems. The advantages of SOTFN-SV are verified by comparisons with the results of these methods.en_US
dc.language.isoen_USzh_TW
dc.relationIeee Transactions on Fuzzy Systemsen_US
dc.relation.ispartofseriesIeee Transactions on Fuzzy Systems, Volume 15, Issue 5, Page(s) 998-1008.en_US
dc.relation.urihttp://dx.doi.org/10.1109/tfuzz.2007.894980en_US
dc.subjectcomposite kernelen_US
dc.subjectfuzzy clusteringen_US
dc.subjectfuzzy neural network (FNN)en_US
dc.subjectskinen_US
dc.subjectcolor segmentationen_US
dc.subjectsupport vector machine (SVM)en_US
dc.subjectTakagi-Sugenoen_US
dc.subject(TS)-type fuzzy systemsen_US
dc.subjectneural-networken_US
dc.subjectmachinesen_US
dc.subjectsystemsen_US
dc.subjectcoloren_US
dc.titleA self-organizing TS-type fuzzy network with support vector learning and its application to classification problemsen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1109/tfuzz.2007.894980zh_TW
item.grantfulltextnone-
item.openairetypeJournal Article-
item.languageiso639-1en_US-
item.fulltextno fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:電機工程學系所
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