摘要
针对实施邻域风险最小化原则的邻域支持向量解算法,根据被错分样本一定是支持向量提出一种利用支持向量删除训练样本中难学习样本的修剪算法;依据最大似然原则对已有的高斯邻域函数参数取值方法进行改进.初步实验表明,训练样本的修剪与邻域函数参数取值方法的改进可明显提高邻域支持向量解算法的泛化能力,比SVM测试准确率提高0.5%左右.
Two improvements are introduced into vicinal-risk-minimization based support vector algorithm. Since the misclassified samples must be support vectors, a scheme for pruning hardto-learn samples from the training set based on support vectors is presented. The parameter＇s determination of Gaussian vicinal function is proposed to be modified, based on the maximum likelihood criterion. Preliminary experimental results show that the pruning scheme and improvement of the parameter＇s determination of vicinal function much improved Vicinal SV algorithm＇s generality, and can outperform SVM by about 0. 5% in test accuracy.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2005年第11期967-970,共4页
Transactions of Beijing Institute of Technology
基金
国家"九七三"计划项目(G1998030414)
