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Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/9829

Title: A genetic algorithm-based multi-class support vector machine for Mongolian character recognition
???metadata.dc.creator???: Batsaikan, O.
Ho, C. K.
Singh, Y. P.
Keywords: Support vector machines
Genetic algorithms
Classification
Máquinas de vetores de suporte
Algoritmos genéticos
Classificação
Publisher: Editora da UFLA
???metadata.dc.date???: 1-Mar-2009
Citation: BATSAIKAN, O.; HO, C. K.; SINGH, Y. P. A genetic algorithm-based multi-class support vector machine for Mongolian character recognition. INFOCOMP: Journal of Computer Science, Lavras, v. 8, n. 1, p. 1-7, Mar. 2009.
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.
Other Identifiers: http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/244
???metadata.dc.language???: eng
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