Sparse approximation using least squares support vector machines

By Johan A. K. Suykens, Lukas Lukas, Joos Vandewalle. 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000

336 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/22c1af9ee1e6f33ca583f895e6615ae77f04e061

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Haifeng Wang
2. D. Hu
3. B. De Moor
4. Qinghua Hu
5. Zongxia Xie

In Computer Vision and Pattern Recognition

1. Haifeng Wang
2. D. Hu
3. Qinghua Hu
4. Zongxia Xie
5. Johan A. K. Suykens
6. Joos Vandewalle
7. B. De Moor
8. Bart Baesens

In Signal Processing

1. Haifeng Wang
2. D. Hu
3. B. De Moor
4. Joos Vandewalle
5. Boris N. Khoromskij
6. Qinghua Hu
7. Lieven De Lathauwer
8. Ivan V. Oseledets

Citations in the corpus

• Comparison of SVM and LS-SVM for Regression (2005, background): “LS-SVM FOR NONLINEAR FUNCTION ESTIMATION [3][4][5][8]”
• Optimal control by least squares support vector machines (2001, background): “Hence sparseness is lost in the least squares case but it can be imposed by pruning the support value spectrum (Suykens et al., 2000a,b).”
• Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression (2016, methodology): “Suykens [24] and Wu [25] studied the SVR machine of the GN model based on equality constraints (LS− SVR) and inequality constraints (GN− SVR), respectively, where the noise is zero mean and the same deviation.”