Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/344
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dc.contributor.authorLü, Zhi-Jun-
dc.contributor.authorYang, Jian-guo-
dc.contributor.authorXiang, Qian-
dc.contributor.authorWang, Xiao-ling-
dc.date.accessioned2008-03-12T11:12:35Z-
dc.date.available2008-03-12T11:12:35Z-
dc.date.issued2007-06-
dc.identifier.issn0971-0426-
dc.identifier.urihttp://hdl.handle.net/123456789/344-
dc.description173-178en_US
dc.description.abstractSupport vector machines (SVMs) models have been presented for predicting worsted yarn properties using SVM regression algorithms. Model selection which amounts to search in hyper-parameter space is performed to study the suitable parameter conditions. The predictive powers of the SVM models have been estimated and the results are compared with ANN models. It is observed that under the small population circumstances, SVM models are still capable of maintaining the stability of predictive accuracy, and more suitable for noisy and dynamic spinning process.en_US
dc.language.isoen_USen_US
dc.publisherCSIRen_US
dc.relation.ispartofseriesInt.Cl.⁸ G06Fen_US
dc.sourceIJFTR Vol.32(2) [June 2007]en_US
dc.subjectArtificial neural networksen_US
dc.subjectKernel functionen_US
dc.subjectStructure risk minimizationen_US
dc.subjectSupport vector machinesen_US
dc.subjectWorsted yarnen_US
dc.titleSupport vector machines for predicting worsted yarn propertiesen_US
dc.typeArticleen_US
Appears in Collections:IJFTR Vol.32(2) [June 2007]

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