| 复杂分类问题支持向量机的简化 |
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| 引用本文： | 方景龙,陈铄,潘志庚,梁荣华.复杂分类问题支持向量机的简化[J].电子学报,2007,35(5):858-861. |
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| 作者姓名： | 方景龙 陈铄 潘志庚 梁荣华 |
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| 作者单位： | 1. 杭州电子科技大学图形图象研究所,浙江杭州 310018;2. 浙江大学CAD & CG国家重点实验室,浙江杭州 310027;3. 浙江工业大学信息工程学院,浙江杭州 310014 |
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| 基金项目： | 国家自然科学基金，国家重点基础研究发展计划(973计划)，浙江省自然科学基金 |
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| 摘 要： | 对于复杂分类问题,不可避免的会有错分情况,此时支持向量机的支持向量较多,影响了识别速度.为了解决这个问题,我们提出了基于最小错分间隔的分类思想,并在此基础上得出了一种新的简化支持向量机.与普通支持向量机相比,这种简化支持向量机有较少的支持向量、较高的识别速度,而且实验结果表明,它的识别精度完全可以与普通支持向量机的识别精度相媲美,甚至更优.
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| 关 键 词： | 支持向量机 模式识别 支持向量缩减 |
| 文章编号： | 0372-2112（2007）05-0858-04 |
| 收稿时间： | 2006-05-11 |
| 修稿时间： | 2006-05-112006-07-28 |
A Simplification to Support Vector Machine for Complicated Recognition Problem |
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| FANG Jing-long,CHEN Shuo,PAN Zhi-geng,LIANG Rong-hua.A Simplification to Support Vector Machine for Complicated Recognition Problem[J].Acta Electronica Sinica,2007,35(5):858-861. |
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| Authors: | FANG Jing-long CHEN Shuo PAN Zhi-geng LIANG Rong-hua |
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| Affiliation: | 1. Institute of Graphics and Image,Hangzhou Dianzi University,Hangzhou,Zhejiang 310018,China;2. State Key Laboratory of CAD & CG,Zhejiang University,Hangzhou,Zhejiang 310027,China;3. College of Information Engineering,Zhejiang University of Technology,Hangzhou,Zhejiang 310014,China |
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| Abstract: | For complicated recognition problem,the number of support vectors is large and recognition speed is low,because some sample were divided into section by error this time.To solve this problem,a method is bought to simplify the support vector machines based the minimal misestimate margin idea.Experiments show that this new support vector machine not only reduces the number of support vectors and recognition time but also has the same accuracy as(even better than) traditional support vector machine. |
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| Keywords: | support vector machine pattern recognition support vector pruning |
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