陈增照, 杨扬, 董才林, 何秀玲. 支持向量机动态学习方法及其在票据识别中的应用[J]. 工程科学学报, 2006, 28(2): 199-202. DOI: 10.13374/j.issn1001-053x.2006.02.045
引用本文: 陈增照, 杨扬, 董才林, 何秀玲. 支持向量机动态学习方法及其在票据识别中的应用[J]. 工程科学学报, 2006, 28(2): 199-202. DOI: 10.13374/j.issn1001-053x.2006.02.045
CHEN Zengzhao, YANG Yang, DONG Cailin, HE Xiuling. A dynamical learning method with SVM and its application on bank slip recognition[J]. Chinese Journal of Engineering, 2006, 28(2): 199-202. DOI: 10.13374/j.issn1001-053x.2006.02.045
Citation: CHEN Zengzhao, YANG Yang, DONG Cailin, HE Xiuling. A dynamical learning method with SVM and its application on bank slip recognition[J]. Chinese Journal of Engineering, 2006, 28(2): 199-202. DOI: 10.13374/j.issn1001-053x.2006.02.045

支持向量机动态学习方法及其在票据识别中的应用

A dynamical learning method with SVM and its application on bank slip recognition

  • 摘要: 介绍了用支持向量机(SVM)进行动态学习训练的方法.解决了在机器学习过程中,训练样本获取比较困难,样本可随外界条件改变而变化的问题.实践证明,使用该方法可以动态跟踪样本的变化,保证SVM分类器的最优性能.利用该方法设计的银行票据OCR系统的实际应用说明了该方法的有效性.

     

    Abstract: This paper introduces a dynamical learning method using support vector machine (SVM). This method can solve such machine learning problems as the difficulties in gathering training samples and the change of samples with outer environment. It is proved that SVM classifiers can achieve optimal performance after using this method in tracking the change of samples. A bank slip OCR system designed by this method proves the validity.

     

/

返回文章
返回