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https://www.sciencedirect.com/science/article/pii/S0925231201006440
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem.Cited by: 1444
https://www.researchgate.net/publication/220552601_Weighted_Least_Squares_Support_Vector_Machines_robustness_and_sparse_approximation
In least squares support vector machines (LS-SVMs) for function estimation Vapnik's ε-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression.
https://core.ac.uk/download/pdf/34266169.pdf
Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem. However, sparseness is lost in the LS-SVM case and
https://ece.uwaterloo.ca/~ece602/Projects/2018/Project12/main.html
Apr 23, 2018 · To optimal a linear Karush-Kuhn-Tucker system, Least squares support vector machines with a least squares cost function is considered as an optimal choice. Since the SVM involves equality instead of inequality which is a good option to solve the problem of KKT.
https://www.worldscientific.com/doi/10.1142/9789812776655_0005
Weighted LS-SVMs. Robust cross-validation . M-Estimators. L-Estimators. Efficiency-robustness trade-off. A robust and efficient cross-validation score function ... Details; Recommended Least Squares Support Vector Machines. Metrics. Downloaded 38 times History. Loading ... Close Figure Viewer. Browse All Figures Return to Figure Change zoom ...
https://www.academia.edu/7096839/Weighted_least_squares_support_vector_machines_robustness_and_sparse_approximation
Weighted least squares support vector machines: robustness and sparse approximation
https://onlinelibrary.wiley.com/doi/full/10.1002/asjc.2208
This article proposes an adaptive path following control method based on least squares support vector machines (LS‐SVM) to deal with parameter changes of the motion model. The path following controller consists of two components: the online identification of varying parameters and model predictive control (MPC) using the adaptively identified ...Cited by: 1
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