支持向量机算法实现人民币序列号识别

人民币序列号识别是一个小样本、非线性和高维模式识别问题，是当前模式识别中的难题之一，具有重要研究意义和实用价值。主要研究了统计学习理论中支持向量机的二次优化算法，并将支持向量机应用于货币序列号的机器识别中。将次序最小优化算法构建的支持向量机用于序列号识别，充分发挥了支持向量机解决小样本、非线性和高维模式识别问题的优点。支持向量机（SVM）实验结果表明，这种支持向量机货币识别方法具有较高的可实现性和识别精度。

Application of Support Vector Machines in Serial Number Identifying on RMB Banknote

Serial numbers identification，a scared samples，nonlinear and high dimensions pattern recognition problem is one of the difficult problems of modern pattern recognition and of specific research significance and practical value. This thesis studies the support vector machine and multi-class classification in the statistical learning theory，and applies the support vector machine into the machine paper serial numbers recognition. The support vector machine composed by least sequence algorithm has been used in paper currency identification，shows the advantages of capability in dealing with seared samples，nonlinear and high dimensions. The experiments proved that support vector machine has a high feasibility and recognition accuracy.