The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses
Abstract
A variety of metamodeling techniques have been developed in the past decade to reduce the computational expense of computer-based analysis and simulation codes. Metamodeling is the process of building a “model of a model” to provide a fast surrogate for a computationally expensive computer code. Common metamodeling techniques include response surface methodology, kriging, radial basis functions, and multivariate adaptive regression splines. In this paper, we investigate support vector regression (SVR) as an alternative technique for approximating complex engineering analyses. The computationally efficient theory behind SVR is reviewed, and SVR approximations are compared against the aforementioned four metamodeling techniques using a test bed of 26 engineering analysis functions. SVR achieves more accurate and more robust function approximations than the four metamodeling techniques, and shows great potential for metamodeling applications, adding to the growing body of promising empirical performance of SVR.
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Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,281
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,703
The Nature of Statistical Learning Theory
Vapnik, V. N. (2000) · Springer eBooks · doi:10.1007/978-1-4757-3264-1 · Cited by 10,696
Multivariate Adaptive Regression Splines
Friedman, J. H. (1991) · The Annals of Statistics · doi:10.1214/aos/1176347963 · Cited by 8,000
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,415
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,218
Design and Analysis of Computer Experiments
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989) · Statistical Science · doi:10.1214/ss/1177012413 · Cited by 6,919
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,308
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Viana, F. A. C., Haftka, R. T., & Steffen, V. (2009) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-008-0338-0 · Cited by 420
Ding, Z., Chen, W., Hu, T., & Xu, X. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116660 · Cited by 85
The use of a finger-worn accelerometer for monitoring of hand use in ambulatory settings
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Metadata
- Authors
- Clarke, S. M., Griebsch, J. H., & Simpson, T. W.
- Venue
- Journal of Mechanical Design
- Publisher
- ASME International
- Published
- 2004-08-13
- Volume
- 127
- Issue
- 6
- Pages
- 1077-1087
- ISSN
- 0161-8458 (ISSN-L)
- 1050-0472
- 1528-9001
- OpenAlex
- W4236993164
- Cited by
- 523
- References
- 29
- FWCI
- 5.10
- Type
- journal article
- Language
- en
- Open access
- closed
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