Kernel least-squares models using updates of the pseudoinverse
Neural Computation
Online Least Squares Support Vector Machines Based on Wavelet and Its Applications
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Recursive reduced least squares support vector regression
Pattern Recognition
Least squares one-class support vector machine
Pattern Recognition Letters
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
Updates for nonlinear discriminants
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Online prediction of time series data with kernels
IEEE Transactions on Signal Processing
A novel pruning algorithm for self-organizing neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Local weighted LS-SVM online modeling and the application in continuous processes
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
A fast method of feature extraction for kernel MSE
Neurocomputing
Evolution strategies based adaptive Lp LS-SVM
Information Sciences: an International Journal
Computers and Electronics in Agriculture
Least squares wavelet support vector machines for nonlinear system identification
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Help-Training for semi-supervised support vector machines
Pattern Recognition
Improved conjugate gradient implementation for least squares support vector machines
Pattern Recognition Letters
Online independent reduced least squares support vector regression
Information Sciences: an International Journal
Pruning least objective contribution in KMSE
Neurocomputing
Fixed budget quantized kernel least-mean-square algorithm
Signal Processing
Twin least squares support vector regression
Neurocomputing
Efficient sparse least squares support vector machines for pattern classification
Computers & Mathematics with Applications
Fast sparse approximation of extreme learning machine
Neurocomputing
Hi-index | 0.01 |
The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an ε-insensitive cost function, meaning that errors smaller than ε remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsparse solution is obtained. The sparseness is imposed by pruning, i.e., recursively solving the approximation problem and subsequently omitting data that has a small error in the previous pass. However, omitting data with a small approximation error in the previous pass does not reliably predict what the error will be after the sample has been omitted. In this paper, a procedure is introduced that selects from a data set the training sample that will introduce the smallest approximation error when it will be omitted. It is shown that this pruning scheme outperforms the standard one.