最小二乘分解算法在车型识别中的应用  

Application of Least Square Decomposition Algorithm in Vehicle Recognition

作　　者：周磊[1] 冯玉田[1] 

机构地区：[1]上海大学通信与信息工程学院,上海200072

出　　处：《计算机仿真》2009年第7期274-277,共4页Computer Simulation

基　　金：上海市重点学科建设项目资助(T0102)

摘　　要：提出一种最小二乘支持向量机的序贯最小分类分解算法。针对最小二乘支持向量机,通过对核函数的相关变换,将二阶的误差信息归结到优化方程的一阶信息中,从而简化运算过程。采用最优函数梯度二阶信息选择工作集,实现最小二乘支持向量机分解算法,提高了算法的收敛性。采用径向基核函数和交叉验证网格搜索的方法验证算法的分类准确性。实验结果表明,提出的分类算法应用于车型识别中,可以得到比其他分类方法更好的分类准确度。In this paper a Sequential Minimal Optimization （SMO） decomposition algorithm of Least Square Support Vector Machines （ LS - SVM） for classification is proposed. Through transforming kernel function, the second - order error information is translated into the first - order information of optimal function to simplify the process of computation for LS - SVM. By using optimal function gradient＇ s second - order information to select working set, the LS - SVM decomposition algorithm is achieved and its convergence is improved. Radial Basis Function （RBF） kernel and grid - search method on cross - validation is used to verify classification accuracy of the algorithm. Results of the experiments show that the algorithm presented in this paper has better classification accuracy than other algorithms when it is applied in vehicle recognition.

关 键 词：最小二乘支持向量机 序贯最小优化 分解算法 车型识别 

分 类 号：TB24[一般工业技术—工程设计测绘]