The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Kernel techniques: From machine learning to meshless methods
Open accessAbstract
Kernels are valuable tools in various fields of numerical analysis, including approximation, interpolation, meshless methods for solving partial differential equations, neural networks, and machine learning. This contribution explains why and how kernels are applied in these disciplines. It uncovers the links between them, in so far as they are related to kernel techniques. It addresses non-expert readers and focuses on practical guidelines for using kernels in applications.
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Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Kernel Methods for Pattern Analysis
Shawe-Taylor, J., & Cristianini, N. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809682 · Cited by 6,587
Aronszajn, N. (1950) · Transactions of the American Mathematical Society · doi:10.1090/s0002-9947-1950-0051437-7 · Cited by 5,380
A two-dimensional interpolation function for irregularly-spaced data
Shepard, D. (1968) · Proceedings of the 1968 23rd ACM national conference on - · doi:10.1145/800186.810616 · Cited by 5,079
Spline Models for Observational Data.
Muller, H.-G., & Wahba, G. (1991) · Journal of the American Statistical Association · doi:10.2307/2290434 · Cited by 4,928
A fast algorithm for particle simulations
Greengard, L., & Rokhlin, V (1987) · Journal of Computational Physics · doi:10.1016/0021-9991(87)90140-9 · Cited by 4,868
A hierarchical O(N log N) force-calculation algorithm
Barnes, J., & Hut, P. (1986) · Nature · doi:10.1038/324446a0 · Cited by 3,698
The partition of unity finite element method: Basic theory and applications
Melenk, J. M., & Babuška, I. (1996) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/s0045-7825(96)01087-0 · Cited by 3,455
Craven, P., & Wahba, G. (1978) · Numerische Mathematik · doi:10.1007/bf01404567 · Cited by 3,388
Cited by
Sukumar, N., & Srivastava, A. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114333 · Cited by 426
Metadata
- Authors
- Schaback, R., & Wendland, H.
- Venue
- Acta Numerica
- Publisher
- Cambridge University Press (CUP)
- Published
- 2006-05
- Volume
- 15
- Pages
- 543-639
- ISSN
- 0962-4929 (ISSN-L)
- 1474-0508
- OpenAlex
- W2089245375
- Cited by
- 289
- References
- 65
- FWCI
- 22.04
- Type
- journal article
- Language
- en
- Open access
- green
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