摘要
为了改进现有支持向量机所确定的边界抗干扰能力差、对噪声数据敏感等问题,减少野点数据对形成支持向量机边界存在的影响,根据各个样本在整个训练样本集中的重要性不同,将各个训练样本的重要性程度值作为权值赋予边界值上,提出了一种基于加权边界调节的支持向量机算法.通过对标准UC I数据集和人工数据集上的仿真实验表明,基于加权边界调节的支持向量机具有较好的野点免疫能力,具有更高的分类精度、更少的支持向量和更好的推广能力.
To improve the anti-jamming capability and noise sensitivity of existing support vector machines, and reduce the influence of the outliers on the hyperplane, an improved support vector machine algorithm is based on a weighted adjustable separating hyperplane. In this algorithm, significant values of various training samples were used as weights to assign them to boundary values. Simulation results using the standard UCI data set and an artificial data set show that the proposed algorithm has better ability to resist disturbance and noise and has much more classification precision and requires fewer support vectors compared with a standard or fuzzy support vector machine.
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2007年第10期1135-1138,共4页
Journal of Harbin Engineering University
基金
国家自然科学基金资助项目(60673131)
黑龙江省自然科学基金资助项目(F2005-02)
关键词
支持向量机
最优超平面
样本重要性
加权边界调节
support vector machine
optimal hyperplane
sample importance
weighted adjustable separating hyperplane
