Recursive reduced least squares support vector regression
Pattern Recognition
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
Help-training semi-supervised LS-SVM
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
Large margin classifiers based on affine hulls
Neurocomputing
Help-Training for semi-supervised support vector machines
Pattern Recognition
Momentum acceleration of least-squares support vector machines
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Improved conjugate gradient implementation for least squares support vector machines
Pattern Recognition Letters
Probability model of covering algorithm (PMCA)
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Self-advising support vector machine
Knowledge-Based Systems
Efficient sparse least squares support vector machines for pattern classification
Computers & Mathematics with Applications
Fast sparse approximation of extreme learning machine
Neurocomputing
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The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.