中文摘要：

针对手持式字符识别系统开发中系统对实时性要求较高、系统资源有限以及传统的支持向量机（SVM）分类方法难以同时满足识别率和识别速度的缺点,提出一种快速的SVM（FCSVM）分类算法。对支持向量集采用变换的方式,用少量的支持向量代替全部支持向量进行分类计算,在保证不损失分类精度的前提下使得分类速度较传统SVM算法有较大提高。实验结果表明,FCSVM算法较大幅度地减少了计算复杂度,提高了分类速度,尤其在嵌入式系统中效果更加明显。

英文摘要：

During the development of hand-held character recognition devices,the conflict of limited resources and high demand for real-time makes the traditional support vector machine（SVM） classification method fail to meet the requirements of recognition speed and recognition rate at the same time.A fast classification algorithm of support vector machine（FCSVM） is proposed.After the transformation on the full set of support vectors,a subset of support vectors instead of the full set of support vectors is used in classification.The speed of classification is much faster than that of conventional SVM under the condition that the precision of classification does not decline.The experimental results show that the improved fast classification algorithm can remarkably reduce the computation complexity and improve the classification speed,and the effects are more obvious in embedded systems.