Support vector regression for determining the minimum zone sphericity

International Journal of Advanced Manufacturing Technology

Volumn 35, Issue 9-10, 2008, Pages 916-923

  Balakrishna, Poornima ^a   Raman, Shivakumar ^a   Trafalis, Theodore B ^a   Santosa, Budi ^a  

^a UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

Coordinate metrology; Minimum zone spherical tolerance; Radial basis kernel function; Support vector regression

Indexed keywords

ALGORITHMS; COMPUTATIONAL GEOMETRY; LEAST SQUARES APPROXIMATIONS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

COORDINATE METROLOGY; RADIAL BASIS KERNEL FUNCTION; SUPPORT VECTOR REGRESSION;

SUPPORT VECTOR MACHINES;

EID: 38349149309     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-006-0774-1     Document Type: Article

References (34)

1. ASME Y 14.5 M (1994) 1995, "Dimensioning and Tolerancing," New York, NY: The American Society of Manufacturing Engineers
2. Prakasvudhisarn C, Raman S (2004) Framework for cone feature measurement using coordinate measuring machines. Transactions of the ASME, J Manuf Sci Eng 126(1):169-177
3. Aguirre J, Raman S (2005) Torus Inspection Using Coordinate Sampling. Transactions of the ASME, J Manuf Science Eng 127(1)
4. Malyscheff AM, Trafalis TB, Raman S (2002) From support vector machine learning to the determination of the minimum enclosing zone. Comput Ind Eng 42:59-74
5. Prakasvudhisarn C, Trafalis TB, Raman S (2003) Support vector regression for determination of minimum zone. Transactions of the ASME, J Manuf Sci Eng 125(4):736-739
6. Murthy TSR, Abdin SZ (1980) Minimum zone evaluation of surfaces. Int J Mac Tool Des Res 20:123-136
7. Shunmugam MS (1986) On assessment of geometric errors. Int J Prod Res 24(2):413-425
8. Shunmugam MS (1987) Comparison of linear and normal deviations of forms of engineering surfaces. Precis Eng 9(2):96-102
9. Chetwynd DG (1985) Applications of linear programming to engineering metrology. Proc Inst Mec Eng 199(B2):93-100
10. Chetwynd DG (1979) Roundness measurements using limacons. Precis Eng 1(3):137-141
11. Carr K, Ferriera P (1995) Verification of form tolerances part I: basic issues, flatness, and straightness. Precis Eng 17:131-143
12. Carr K, Ferriera P (1995) Verification of form tolerances part II: cylindricity and straightness of a median line. Precis Eng 17:144-156
13. Murthy TSR, Raghunatha Rao B, Abdin SZ (1979) Evaluation of spherical surfaces. Wear 57:167-184
14. Kanada T (1995) Evaluation of spherical form errors - computation of sphericity by means of minimum zone method and some examinations with using simulated data. Precis Eng 17(4):281-289
15. Kanada T (1997) Estimation of sphericity by means of statistical processing for roundness of spherical parts. Precis Eng 20:117-122
16. Cristianini N, Shawe-Taylor J (2000) An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press
17. Fisher RA (1947) The Design of Experiments. Oliver and Boyd, Edinburgh. 4th edn.
18. Kempthorne O (1952) The Design and Analysis of Experiments. Wiley
19. Schölkopf B, Smola AJ (2002) Learning with Kernels - Support Vector Machines, Regularization, Optimization and Beyond. The MIT Press, Cambridge, Massachusetts, London, England
20. Vapnik V (1995) The Nature of Statistical Learning Theory. Springer, Berlin Heidelberg New York
21. Schölkopf B, Smola AJ, Williamson R, Bartlett P (1998) New Support Vector Algorithms. NeuroCOLT2 Technical Report Series, NC2-TR-1998-031, Nov. 1998. ( http://www.neurocolt.com )
22. Schölkopf B, Bartlett P, Smola A, Williamson R (1998) "Shrinking the Tube: A New Support Vector Regression Algorithm," In: Advances in Neural Information Processing Systems, 11. MIT Press, Cambridge, MA, pp. 330-336
23. Schölkopf B, Bartlett P, Smola A, Williamson R (1998) Support Vector Regression with Automatic Accuracy Control. In: Niklasson L, Bodén M, Ziemke T (eds.) Proceedings of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing. Springer Berlin Heidelberg New York, pp. 111-116
24. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113-126
25. Trafalis TB, Malyscheff AM (2004) Optimal Selection of the Regression Kernel Matrix with Semidefinite Programming. In: Floudas CA, Pardalos PM (eds.) Frontiers in Global Optimization, Nonconvex Optimization and its Applications 24 Kluwer Academic Publishers pp. 575-584
26. Keerthi SS, Lin C-J (2003) Asymptotic behaviors of support vector machines with gaussian kernel. Neural Comput 15:1667-1689, © Massachusetts Institute of Technologys
27. Smola AJ, Schölkopf B (1998) From Regularization Operators to Support Vector Kernels. In: Proceedings of the 1997 conference on Advances in Neural Information Processing Systems 10. MIT Press, Cambridge, MA, USA, pp. 343-349
28. Ravindran A, Reklaitis GV, Ragsdell KM (1983) Engineering Optimization: Methods and Applications. Wiley
29. Chang C-C, Lin C-J (2002) Training v-support vector regression: theory and algorithms. Neural Comput 1, 14(8):1959-1977, August
30. Shunmugam MS (1991) Criteria for computer-aided form evaluation. Transactions of the ASME, J Eng Ind 113:233-238, May
31. Elzinga DJ, Hearn DW (1972) The minimum covering sphere problem. Manage Sci 19(1):96-104, September
32. SPIDER©, Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. http://www.kyb.tuebingen.mpg.de/bs/people/spider/main.html
33. MATLAB®, version 6.5.0.180913a Release 13, June 18, 2002
34. Lee G, Mou J, Shen Y (1997) Sampling strategy design for dimensional measurement of geometric features using coordinate measuring machine. Int J Mach Tool Manuf 37(7):917-934