Abstract
This paper presents the principle of Support Vector Machine (SVM) that is currently gaining a great interest for solving problems about data classification and pattern recognition. Afterward, an application of SVMs for data categorization is proposed through a Thai hand-written consonant recognition program. This part begins with a data preparation, a training method and a data modeling approach. Finally, the obtained model will be tested for evaluating the performance of data classification. The experimental results have shown that the SVMs can achieve a high accuracy rate, more than 86%, on real dataset.
Keywords : Support Vector Machines, Data Classification, Pattern Recognition, Machine Learning, Thai Consonant Recognition
This paper presents the principle of Support Vector Machine (SVM) that is currently gaining a great interest for solving problems about data classification and pattern recognition. Afterward, an application of SVMs for data categorization is proposed through a Thai hand-written consonant recognition program. This part begins with a data preparation, a training method and a data modeling approach. Finally, the obtained model will be tested for evaluating the performance of data classification. The experimental results have shown that the SVMs can achieve a high accuracy rate, more than 86%, on real dataset.
Keywords : Support Vector Machines, Data Classification, Pattern Recognition, Machine Learning, Thai Consonant Recognition
