一种改进的样例约简支持向量机

南京大学学报(自然科学版) ›› 2013, Vol. 49 ›› Issue (5): 596–602.

一种改进的样例约简支持向量机

翟俊海1,2王婷婷1王熙照1,2

出版日期:2014-01-21 发布日期:2014-01-21
作者简介:(河北大学数学与计算机学院保定071002; 2. 河北省机器学习与计算智能重点实验室保定071002)
基金资助:
国家自然科学基金 (61170040)，河北省自然科学基金 (F2013201110, F2013201220)，河北大学自然科学基金 (2011-228043)，河北大学教育教学改革研究项目(JX07-Y-27)

An mproved instance reduction support vector machine

Zhai Jun-Hai1,2, Wang Ting-Ting1, Wang Xi-Zhao1,2

Online:2014-01-21 Published:2014-01-21
About author:(1. College of Mathematics and Computer Science, Hebei University, Baoding, 071002, China; 2. Key Laboratory of Machine Learning and Computational Intelligence, Baoding, 071002, China)

摘要/Abstract

摘要： 在以前工作的基础上，提出了一种改进的样例约简支持向量机，利用相容粗糙集方法求属性约简的边界域，并从中选择样例作为候选支持向量训练支持向量机该方法的特点是可同时对属性和样例进行约简。实验结果证实了这种方法的有效性，能有效地减少存储空间和执行时间。

Abstract: Based on the previous work, an improved instance reduction support vector machine was proposed in this paper. By employing tolerance rough set technique, the boundary region of the reduct is calculated and the candidate support vectors used for training support vector machine are selected from this region. Simultaneous calculations of attribute reduction and instance reduction characterize our method. The experimental results show that the proposed method is effective and can efficiently reduce the computational complexities of both time and space

翟俊海1,2王婷婷1王熙照1,2. 一种改进的样例约简支持向量机[J]. 南京大学学报(自然科学版), 2013, 49(5): 596–602.

Zhai Jun-Hai1,2, Wang Ting-Ting1, Wang Xi-Zhao1,2. An mproved instance reduction support vector machine[J]. Journal of Nanjing University(Natural Sciences), 2013, 49(5): 596–602.

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