| 改进的邻域支持向量解算法 |
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| 引用本文： | 闭乐鹏,郑志蕴,宋瀚涛,陆玉昌. 改进的邻域支持向量解算法[J]. 北京理工大学学报, 2005, 25(11): 967-970 |
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| 作者姓名： | 闭乐鹏 郑志蕴 宋瀚涛 陆玉昌 |
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| 作者单位： | 北京理工大学,信息科学技术学院计算机科学工程系,北京,100081;清华大学,智能技术与系统国家重点实验室,北京,100084 |
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| 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.
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| 关 键 词： | 邻域支持向量解 修剪样本 高斯函数 |
| 文章编号： | 1001-0645(2005)11-0967-04 |
| 收稿时间： | 2004-12-31 |
| 修稿时间： | 2004-12-31 |
An Improved Vicinal SV Algorithm |
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| BI Le-peng,ZHENG Zhi-yun,SONG Han-tao and LU Yu-chang. An Improved Vicinal SV Algorithm[J]. Journal of Beijing Institute of Technology(Natural Science Edition), 2005, 25(11): 967-970 |
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| Authors: | BI Le-peng ZHENG Zhi-yun SONG Han-tao LU Yu-chang |
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| Affiliation: | 1. Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China; 2. State Key Laboratory of Intelligent Technology and System, Tsinghua University, Beijing 100084, China |
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| Abstract: | Two improvements are introduced into vicinal-risk-minimization based support vector algorithm.Since the misclassified samples must be support vectors,a scheme for pruning hard-to-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. |
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| Keywords: | vicinal SVM pruning samples gaussian function |
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