The nature of statistical learning theory
The nature of statistical learning theory
Efficient Text Classification by Weighted Proximal SVM
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Expert Systems with Applications: An International Journal
Fuzzy support vector machine for multi-class text categorization
Information Processing and Management: an International Journal
Identification of EMG signals using discriminant analysis and SVM classifier
Expert Systems with Applications: An International Journal
Computers and Operations Research
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Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method's efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with simulations and the best method is validated with experimental data.