Self-organizing maps
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Sparseness of support vector machines
The Journal of Machine Learning Research
Multiclass reduced-set support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
IP-LSSVM: A two-step sparse classifier
Pattern Recognition Letters
A novel heuristic for building reduced-set SVMs using the self-organizing map
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
| Hi-index | 0.00 |
Opposite Maps (OM) is a method that can be used to induce sparse SVM-based and LS-SVM-based classifiers. The main idea behind the OM method is to train two Self-Organizing Maps (SOM), one for each class, $\mathcal{C}_{-1}$ and $\mathcal{C}_{+1}$, in a binary classification context and then, for the patterns of one class, say $\mathcal{C}_{-1}$, to find the closest prototypes among those belonging to the SOM trained with patterns of the other class, say $\mathcal{C}_{+1}$. The subset of patterns mapped to the selected prototypes in both SOMs form the reduced set to be used for training SVM and LSSVM classifiers. In this paper, an iterative method based on the OM, called Fast Opposite Maps, is introduced with the aim of accelerating OM training time. Comprehensive computer simulations using synthetic and real-world datasets reveal that the proposed approach achieves similar results to the original OM, at a much faster pace.