Monitoring nonlinear profiles using support vector machines

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4756 LNCS, Issue , 2007, Pages 574-583

  Moguerza, Javier M ^a   Muñoz, Alberto ^b   Psarakis, Stelios ^c  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^c ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS   (Greece)

Author keywords

Kernel methods; Statistical process control; Support vector machines

Indexed keywords

DATA REDUCTION; MATHEMATICAL MODELS; NONLINEAR ANALYSIS; STATISTICAL METHODS;

FUNCTIONAL DATA ANALYSIS; STATISTICAL PROCESS CONTROL (SPC);

SUPPORT VECTOR MACHINES;

EID: 38449114581     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (18)

1. Cressie, N.: Statistics for Spatial Data. Wiley, Chichester (1993)
2. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
3. Ding, Y., Zeng, L., Zhou, S.: Phase I Analysis for Monitoring Nonlinear Profiles in Manufacturing Processes. Journal of Quality Technology 38(3), 199-216 (2005)
4. Draper, N., Smith, H.: Applied Regression Analysis. Wiley, Chichester (1997)
5. Jensen, W.A., Birch, J.B., Woodall, W.H.: Profile Monitoring via Linear Mixed Models. Journal of Quality Technology (to appear, 2007)
6. Keerthi, S.S., Lin, C.: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 15, 1667-1689 (2003)
7. Moguerza, J.M., Muñoz, A.: Support Vector Machines with Applications. Statistical Science 21(3), 322-336 (2006)
8. Moguerza, J.M., Muñoz, A.: Rejoinder to Support Vector Machines with Applications. Statistical Science 21(3), 358-362 (2006)
9. Montgomery, D.C.: Introduction to Statistical Quality Control, 2nd edn. Wiley, Chichester (1990)
10. Muñoz, A., Moguerza, J.M.: Estimation of high density regions using One-Class Neighbor Machines. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(3), 476-480 (2006)
11. Ramsay, J.O., Silverman, B.W.: Functional Data Analysis. Springer, Heidelberg (1997)
12. Tikhonov, A.N., Arsenin, V.Y.: Solutions of ill-posed problems. John Wiley & Sons, New York (1977
13. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
14. Wahba, G.: Spline Bases, Regularization, and Generalized Cross Validation for Solving Approximation Problems with Large Quantities of Data. In: Cheney, W. (ed.) Approximation Theory III, pp. 905-912. Academic Press, London (1980)
15. Walker, E.: Determining the Smoothing Parameter in Penalized Regression Splines Using Model Selection Criteria. Talk to the IMPS, Osaka, Japan (2001)
16. Walker, E., Wright, W.P.: Comparing Curves with Additive Models. Journal of Quality Technology 34(1), 118-129 (2002)
17. Williams, J.D., Woodall, W.H., Birch, J.B.: Statistical Monitoring of Nonlinear Product and Process Quality Profiles. Quality and Reliability Engineering International (to appear, 2007)
18. Woodall, W.H., Spitzner, D.J., Montgomery, D.C., Gupta, S.: Using Control Charts to Monitor Process and Product Quality Profiles. Journal of Quality Technology 36, 309-320 (2004)