On the Selection of the Bandwidth Parameter for the k‐Chart
作者： Maria L. Weese , Waldyn G. Martinez , L. Allison Jones-Farmer
DOI: 10.1002/QRE.2123
关键词: Gaussian function 、 Data mining 、 Support vector machine 、 Computer science 、 Chart 、 Algorithm 、 Bandwidth (signal processing) 、 One-class classification 、 Data description
摘要: The k-chart, based on support vector data description, has received recent attention in the literature. We review four different methods for choosing bandwidth parameter, s, when k-chart is designed using Gaussian kernel. provide results of extensive Phase I and II simulation studies varying method parameter along with size distribution sample data. In very limited cases, performed as desired. general, we are unable to recommend use a or process monitoring study its current form. Copyright © 2017 John Wiley & Sons, Ltd.
sci-hub.se 参考文章(12)
L Allison Jones, William H Woodall, None, The Performance of Bootstrap Control Charts Journal of Quality Technology. ,vol. 30, pp. 362- 375 ,(1998) , 10.1080/00224065.1998.11979872
Marco Grasso, Bianca Maria Colosimo, Quirico Semeraro, Massimo Pacella, A Comparison Study of Distribution‐Free Multivariate SPC Methods for Multimode Data Quality and Reliability Engineering International. ,vol. 31, pp. 75- 96 ,(2015) , 10.1002/QRE.1708
Walid Gani, Mohamed Limam, Performance Evaluation of One‐Class Classification‐based Control Charts through an Industrial Application Quality and Reliability Engineering International. ,vol. 29, pp. 841- 854 ,(2013) , 10.1002/QRE.1440
David M.J. Tax, Robert P.W. Duin, Support Vector Data Description Machine Learning. ,vol. 54, pp. 45- 66 ,(2004) , 10.1023/B:MACH.0000008084.60811.49
Walid Gani, Hassen Taleb, Mohamed Limam, An assessment of the kernel‐distance‐based multivariate control chart through an industrial application Quality and Reliability Engineering International. ,vol. 27, pp. 391- 401 ,(2011) , 10.1002/QRE.1117
Richard G. Brereton, Gavin R. Lloyd, Support Vector Machines for classification and regression Analyst. ,vol. 135, pp. 230- 267 ,(2010) , 10.1039/B918972F
S. Khazai, A. Safari, B. Mojaradi, S. Homayouni, Improving the SVDD Approach to Hyperspectral Image Classification IEEE Geoscience and Remote Sensing Letters. ,vol. 9, pp. 594- 598 ,(2012) , 10.1109/LGRS.2011.2176101
David M.J Tax, Robert P.W Duin, Support vector domain description Pattern Recognition Letters. ,vol. 20, pp. 1191- 1199 ,(1999) , 10.1016/S0167-8655(99)00087-2
Xianghui Ning, Fugee Tsung, Improved design of kernel distance–based charts using support vector methods Iie Transactions. ,vol. 45, pp. 464- 476 ,(2013) , 10.1080/0740817X.2012.712237
Robert P. W. Duin, David M. J. Tax, Uniform object generation for optimizing one-class classifiers Journal of Machine Learning Research. ,vol. 2, pp. 155- 173 ,(2002) , 10.5555/944790.944809