ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Recursive reduced least squares support vector regression
Pattern Recognition
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
Comparing measures of sparsity
IEEE Transactions on Information Theory
A new support vector machine for microarray classification and adaptive gene selection
ACC'09 Proceedings of the 2009 conference on American Control Conference
Sparse support vector regressors based on forward basis selection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Optimized fixed-size kernel models for large data sets
Computational Statistics & Data Analysis
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
An effective method of pruning support vector machine classifiers
IEEE Transactions on Neural Networks
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
A fast method of feature extraction for kernel MSE
Neurocomputing
Help-Training for semi-supervised support vector machines
Pattern Recognition
Expert Systems with Applications: An International Journal
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
Expert Systems with Applications: An International Journal
Pruning least objective contribution in KMSE
Neurocomputing
A novel method of sparse least squares support vector machines in class empirical feature space
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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
Training sparse SVM on the core sets of fitting-planes
Neurocomputing
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In this paper, we present two fast sparse approximation schemes for least squares support vector machine (LS-SVM), named FSALS-SVM and PFSALS-SVM, to overcome the limitation of LS-SVM that it is not applicable to large data sets and to improve test speed. FSALS-SVM iteratively builds the decision function by adding one basis function from a kernel-based dictionary at one time. The process is terminated by using a flexible and stable epsilon insensitive stopping criterion. A probabilistic speedup scheme is employed to further improve the speed of FSALS-SVM and the resulting classifier is named PFSALS-SVM. Our algorithms are of two compelling features: low complexity and sparse solution. Experiments on benchmark data sets show that our algorithms obtain sparse classifiers at a rather low cost without sacrificing the generalization performance