| 一种改进的支持向量机降维方法 |
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| 引用本文： | 申中华,王士同,潘永惠. 一种改进的支持向量机降维方法[J]. 计算机应用与软件, 2008, 25(5): 229-231 |
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| 作者姓名： | 申中华 王士同 潘永惠 |
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| 作者单位： | 江南大学信息工程学院,江苏,无锡,214122 |
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| 摘 要： | 提出了一种基于支持向量机的改进的降维方法.在输入和特征空间中,特征子集的选取分别根据原始特征每一维对分类的贡献来获得.最后,通过将输入和特征空间中的特征选取联合起来,得到了一种改进的降维方法.实验表明:使用这种方法,在保持对分类准确率不受明显的影响的同时,能大大地提高训练和预测的速度.
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| 关 键 词： | 支持向量机 降维 特征选取 人脸检测 |
| 修稿时间： | 2006-06-15 |
IMPROVED DIMENSIONALITY REDUCTION METHOD BASED ON SUPPORT VECTOR MACHINES |
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| Shen Zhong-hua,Wang Shi-tong,Pan Yong-hui. IMPROVED DIMENSIONALITY REDUCTION METHOD BASED ON SUPPORT VECTOR MACHINES[J]. Computer Applications and Software, 2008, 25(5): 229-231 |
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| Authors: | Shen Zhong-hua Wang Shi-tong Pan Yong-hui |
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| Affiliation: | Shen Zhonghua Wang Shitong Pan Yonghui(School of Information Engineering,Jiangnan University,Wuxi 214122,Jiangsu,China) |
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| Abstract: | In this paper we present an improved dimensionality reduction method based on support vector machines(SVMs).In both input and feature space,a subset of features was selected by ranking its contributions to the classification associated to its original features respectively.Accordingly,we developed an improved dimensionality reduction method by the combination of the feature selections in input and feature space.Experiments showed that training SVMs to use the selected subset features,which were obtained by ... |
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| Keywords: | Support vector machines Dimensionality reduction Feature selection Face detection |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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