| 基于RS理论的支持向量机分类模型 |
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| 引用本文： | 唐发明,王仲东,陈绵云.基于RS理论的支持向量机分类模型[J].兵工自动化,2005,24(3):62-64. |
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| 作者姓名： | 唐发明 王仲东 陈绵云 |
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| 作者单位： | 华中科技大学,控制科学与工程系,湖北,武汉,430074 |
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| 基金项目： | 国家自然科学基金
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国防科技预研基金 |
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| 摘 要： | 基于粗糙集(RS)的支持向量机(SVM)分类模型用RS预处理原始样本数据,通过属性和对象的约简消除输入样本数据冗余条件和样本,简化样本数据空间维数.预处理后数据作为样本数据训练SVM,其模型采用模糊离散.用C 编程实现仿真,选用RBF核函数训练SVM,仿真证明该分类模型提高训练速度和分类精度.
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| 关 键 词： | 粗糙集 知识约简 支持向量机 理论 支持向量机 分类模型 Rough Set Theory Based Support Vector Machine Model 分类精度 训练速度 核函数 仿真 编程实现 模糊离散 数据冗余 空间维数 简化 输入样本 条件 约简 对象 |
| 文章编号： | 1006-1576(2005)03-0062-03 |
| 修稿时间： | 2004年10月8日 |
Classification Model of Support Vector Machine Based on Rough Set Theory |
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| TANG Fa-ming,WANG Zhong-dong,CHEN Mian-yun.Classification Model of Support Vector Machine Based on Rough Set Theory[J].Ordnance Industry Automation,2005,24(3):62-64. |
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| Authors: | TANG Fa-ming WANG Zhong-dong CHEN Mian-yun |
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| Abstract: | For classification model of support vector machine (SVM) based on rough set theory, that original sample data is preprocessed with the knowledge reduction algorithm of RS theory, and the redundant condition attributes and conflicting samples are eliminated from the working sample sets to reduce space dimension of sample data. Preprocessed sample data is used as training sample data of SVM, and fuzzy discrete model is used as training model. The emluator was programmed with C , and SVM was trained with RBF function. The simulation results show that the RS SVM model can improve the training speed and precision of classification. |
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| Keywords: | Rough set theory Knowledge reduction Support vector machine |
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