摘要
针对标准的C-SVM(C-support vector machine)算法在处理很多实际分类问题时,对识别错误代价损失差异很大的极端情况表现出的局限性,提出一种通用的广义支持向量机算法。根据识别错误后所付出的代价,可以把最优分类面向代价损失低的一方进行推移,留给代价损失高的一方更大的空间,提高其识别率,从而减小识别错误后带来的代价损失。该方法进一步提高了标准C-SVM的适用性以及样本的正确识别率,将新算法应用到高分辨雷达距离像的识别中,实验证明,广义C-SVM能取得比传统C-SVM更好的识别效果。
Standard C-support vector machine（C-SVM）algorithm has certain limitation when dealing with many factual pattern classification problems,especially in the extreme case such as the recognition error cost loss in great difference.A kind of generalized C-SVM algorithm is introduced.By estimating the cost of the recognition error,optimal separating hyperplane can be translated into the low cost passage,and leaves more space for the high lost cost to increase recognition rate,thus reducing the damage of recognition error.The new method improves the applicability of C-SVM and sample recognition correct rate.When applied to radar high resolution range profile′s recognition,experimental results show that the proposed method can achieve better recognition effect than the traditional method.
出处
《数据采集与处理》
CSCD
北大核心
2015年第2期434-440,共7页
Journal of Data Acquisition and Processing
关键词
广义支持向量机
最优分类面
识别错误
高分辨雷达距离像
generalized C-support vector machine
optimal separating hyperplane
recognition error
radar high resolution range profile
