Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Machine Learning
Sparse least squares support vector training in the reduced empirical feature space
Pattern Analysis & Applications
Support Vector Machines for Pattern Classification
Support Vector Machines for Pattern Classification
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Fast Sparse Approximation for Least Squares Support Vector Machine
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, we propose a novel method of sparse least squares support vector machine (SLS-SVM) that is trained in each class empirical feature space spanned by the independent training data belonging to the associated class. And we determine the decision function in each class empirical feature space. To prevent that the information of other classes is lost because of generating each class empirical feature space separately, we combine the decision functions of all the classes by training LS-SVM in primal form. Using benchmark data sets, we evaluate the effectiveness of the proposed method over the conventional methods.