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一种基于卫向量的简化支持向量机模型 被引量：1

A model for simplification SVM based on guard vectors
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摘要 针对支持向量机(SVM)在处理大规模训练集时,训练速度和分类速度变慢的缺点,提出了一种基于卫向量的简化SVM模型.用对偶变换及求解线性规划方法提取卫向量,缩小训练集规模;在此基础上对训练得到的支持向量集,用线性相关性去除冗余支持向量,从而达到简化目的.对UCI标准数据集的实验表明:在保证不损失分类精度的前提下,该模型一定程度上改进了传统SVM,缩短了学习时间,取得了良好的效果. A simplification SVM model based on guard vectors is proposed for overcoming the slow speed of training and classification for large scale training set. In order to simplify SVM, the methods of dual transform and linear programming are used to distill guard vectors; based on that, the linearly dependent support vectors are eliminated from SV set. The experiments on the UCI database are done with this algorithm. Results show that in the condition of undeclined correct rate, the running time of this model is reduced and better performance than the standard SVM is achieved.
作者 王宇 毛玉欣
机构地区 大连理工大学管理学院
出处 《大连理工大学学报》 EI CAS CSCD 北大核心 2008年第3期446-450,共5页 Journal of Dalian University of Technology
基金 国家自然科学基金资助项目(重点项目70431001)
关键词 卫向量 支持向量机 训练集 支持向量集 guard vector support vector machine training set support vector set
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献12

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大连理工大学学报
2008年 第3期

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