Modification of SVM’s optimal hyperplane based on minimal mistake
-
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.Abstract: Since some value of error penalties C in C-support vector machine (C-SVM) may cause extreme and irrational optimal separating hyperplanes, a new modification of SVM’s optimal hyperplane was proposed. 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 classified precision and enhanced the ability of reducing the outliers and noises’ effect, compared to C-SVM and other modification algorithm.
-
Key words:
- support vector machine /
- optimal separating hyperplane /
- modification
-
[1] Vapnik V N.Statistical learning theory [M].New York:Wiley,1998 [2] Yang Chanyun,Yang J Y,Wang Jianjun.Margin calibration in SVM class-imbalanced learning [J].Neurocomputing,2009,73 (13):397-411 [3] Yeom H G,Jang I H,Sim K B.Variance considered machines:modification of optimal hyperplanes in support vector machines [C]// IEEE International Symposium on Industrial Electronics.Seoul:IEEE,2009:1144-1147 [4] Enrico F,Marco M.Implementing reliable learning through reliable support vector machines [C]//Proceedings 2011 IEEE Symposium on Foundations of Computational Intelligence.Paris:IEEE,2011:100-106 [5] Zhao Xing,Zhou Yanquan,He Huacan.Researches on algorithm for confidence evaluation and decision modification of SVM [C]// International Conference on Natural Language Processing and Knowledge Engineering.Dalian:NLPKE,2009:1-7 [6] 周皓,李少洪.SVM最优分类面相对位置的修正[J].北京航空航天大学学报,2009,35(11):1302-1305 Zhou Hao,Li Shaohong.Relative position modification of SVM’s optimal hyperplane [J].Journal of Beijing University of Aeronautics and Astronautics,2009,35(11):1302-1305(in Chinese) [7] Fung G,Mangasarian O L.Proximal support vector machine classifiers [C]// Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2001:77-86 [8] 刘芸,唐发根,林广艳.一种改进的近似支持向量机算法[J].北京航空航天大学学报,2007,33(9):1090-1093 Liu Yun,Tang Fagen,Lin Guangyan.Robust proximal support vector machine [J].Journal of Beijing University of Aeronautics and Astronautics,2007,33(9):1090-1093(in Chinese) [9] Hao Peiyi.New support vector algorithms with parametric insensitive/margin model [J].Neural Networks,2010,23(1):60-73 -
点击查看大图
计量
- 文章访问数: 1566
- HTML全文浏览量: 123
- PDF下载量: 545
- 被引次数: 0
下载:
