A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
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Abstract
This paper proposes a hybrid genetic algorithm and support vector machine (GA-SVM) approach to address the Mongolian character recognition problem. As the character recognition problem can be considered as a multi-class classification problem, we devise a DAG-SVM classifier. DAG-SVM uses the One-Against-One technique to combine multiple binary SVM classifiers. The GA is used to select the multi-class SVM model parameters. Empirical results demonstrate that the GA-SVM approach is able to achieve good accuracy rate.
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How to Cite
Batsaikan, O., Ho, C. K., & Singh, Y. P. (2009). A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition. INFOCOMP Journal of Computer Science, 8(1), 1–7. Retrieved from https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/244
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