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动态粒度SVM学习算法 被引量：5

Dynamic Granular Support Vector Machine Learning Algorithm
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摘要 粒度支持向量机(GSVM)在处理分布均匀的数据集时较有效,但现实生活中数据集的分布往往是不可预测的,且分布不均匀.文中提出一种动态粒度支持向量机(DGSVM)学习算法,根据粒的不同分布自动粒划分,使SVM可在不同层次的粒上训练.标准数据集上的实验表明,与GSVM相比,DGSVM具有更好的分类性能. Granular support vector machine （ GSVM ） is effective when dealing with distribution uniform datasets. However, the distribution of the dataset in the real world is unpredictable, and the density is uneven. In this paper, a dynamic granular support vector machine learning algorithm （ DGSVM ） is proposed. According to the different distribution of the granules, some granules are divided automatically and SVM training is performed on different levels of granule space. The experimental results on benchmark datasets demonstrate that DGSVM algorithm obtains better classification performance compared with GSVM.
作者 程凤伟 王文剑 郭虎升
机构地区 山西大学计算机与信息技术学院 山西大学计算智能与中文信息处理教育部重点实验室
出处 《模式识别与人工智能》 EI CSCD 北大核心 2014年第4期372-377,共6页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金项目(No.60975035 61273291) 山西省回国留学人员科研项目(No.2012-008) 山西省研究生教育创新项目(No.2013-3001)资助
关键词 粒度支持向量机(GSVM) 不均匀数据集 分布 动态粒度支持向量机(DGSVM) Uneven Dataset Distribution
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21

  • 1Yao Y Y. Perspectives of Granular Computing/! Proc of the IEEE International Conference on Granular Computing. Beijing, China, 2005, 1:85-90.
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二级参考文献29

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共引文献81

同被引文献28

引证文献5

二级引证文献41

模式识别与人工智能
2014年 第4期

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