基于误差最小的SVM最优分类面修正  

Modification of SVM's optimal hyperplane based on minimal mistake

作　　者：蒋觉义[1] 何玉珠[1] 李建宏[2] 

机构地区：[1]北京航空航天大学仪器科学与光电工程学院,北京100191 [2]北京航空航天大学可靠性与系统工程学院,北京100191

出　　处：《北京航空航天大学学报》2012年第11期1483-1486,共4页Journal of Beijing University of Aeronautics and Astronautics

摘　　要：针对C-支持向量机(C-SVM,C-Support Vector Machine)中惩罚系数C可能导致最优分类面不合理的问题,提出基于误差最小的SVM最优分类面修正方法.通过调整正负类分类间隔的约束条件,求解使训练样本总误差最小的偏置系数,并兼顾与正负类误差之差的绝对值的平衡,得到误差最小的更优分类面.实验证明该修正方法与C-SVM及其它修正方法相比,具有较高的分类精度和较强的抗噪声与野值数据干扰能力.Since some value of error penalties C in C-support vector machine （C-SVM） may cause ex treme and irrational optimal separating hyperplanes, a new modification of SVM＇ s optimal hyperplane was pro posed. By modifying the distance restriction of separating hyperplane between positive and negative classes, the bias coefficient was calculated with minimal training samples＇ total error, while the absolute value of the error difference between positive and negative classes was balanced considered, a better separating hyperplane with minimal mistake was obtained. The experimental results show that this algorithm has improved the classi- fied precision and enhanced the ability of reducing the outliers and noises＇ effect, compared to C-SVM and other modification algorithm.

关 键 词：支持向量机 最优分类面 修正 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]