A Weighted Gaussian Kernel Least Mean Square Algorithm-Reference-Cited by-同舟云学术

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A Weighted Gaussian Kernel Least Mean Square Algorithm

Published:2023-04-11 Issue:9 Volume:42 Page:5267-5288
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Moinuddin Muhammad^ORCID,Zerguine Azzedine,Arif Muhammad

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s00034-023-02337-y.pdf

Reference37 articles.

1. K.A. Al-Hujaili, T.Y. Al-Naffouri, M. Moinuddin, The steady-state of the (normalized) lms is schur convex. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4900–4904 (2016). doi: https://doi.org/10.1109/ICASSP.2016.7472609

2. S. An, W. Liu, S. Venkatesh, Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognit. 40(8), 2154–2162 (2007). https://doi.org/10.1016/j.patcog.2006.12.015

3. N. Aronszajn, Theory of reproducing kernels. Trans. Am. Math. Soc. 68(3), 337–404 (1950)

4. C. Brunsdon, Estimating probability surfaces for geographical point data: an adaptive kernel algorithm. Comput. Geosci. 21(7), 877–894 (1995). https://doi.org/10.1016/0098-3004(95)00020-9

5. G.C. Cawley, N.L. Talbot, Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognit. 36(11), 2585–2592 (2003). https://doi.org/10.1016/S0031-3203(03)00136-5

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