Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/344
Title: Support vector machines for predicting worsted yarn properties
Authors: Lü, Zhi-Jun
Yang, Jian-guo
Xiang, Qian
Wang, Xiao-ling
Keywords: Artificial neural networks
Kernel function
Structure risk minimization
Support vector machines
Worsted yarn
Issue Date: Jun-2007
Publisher: CSIR
Series/Report no.: Int.Cl.⁸ G06F
Abstract: Support 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.
Description: 173-178
URI: http://hdl.handle.net/123456789/344
ISSN: 0971-0426
Appears in Collections:IJFTR Vol.32(2) [June 2007]

Files in This Item:
File Description SizeFormat 
FTR 32(2) (2007) 173-178.pdf427.36 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.