基于改进的加权向量机的假币识别算法  

Recognition of counterfeit money based on improved weighted support vector machines

作　　者：林树青[1] 李蓉[1] 

机构地区：[1]华南师范大学增城学院,广东广州511363

出　　处：《计算机时代》2013年第2期34-37,共4页Computer Era

摘　　要：支持向量机在处理样本类别不平衡时,对样本数量少的类别,其分类误差大,针对这一问题提出了一种多核心的加权向量机。将纸币的RGB色彩空间转换到YIQ色彩空间,使用直方图对样本进行特征的提取。在特征空间使用边界区间处理分类问题,加入最小化小样本误识率的机制,使用一种改进的加权支持向量机来识别样本。对样本的不同区域使用不同的核函数。实验表明本算法可行、可靠。To deal with the unbalanced problem in classification, a multi-kernel Support Vector Machines is presented in this paper. RGB color space of note is transformed to the YIQ color space and the characteristics are extracted by using histogram. The classification problem is dealt with by using the boundary interval in eigen-space, and an improved weighted support vector machines are added to minimize the false alarm rate of the small sample. The different kernel functions are used in different regions of the sample. Simulation results show that this algorithm is feasible and reliable.

关 键 词：多核学习 加权支持向量机 纸币识别 惩罚因子 加权值 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]