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| 边界邻近支持向量机* | |
| 引用本文： | 奉国和,李拥军,朱思铭.边界邻近支持向量机*[J].计算机应用研究,2006,23(4):11-12. |
| 作者姓名： | 奉国和 李拥军 朱思铭 |
| 作者单位： | 1. 广州大学城华南师范大学,经济管理学院,广东,广州,510006 2. 华南理工大学,计算机科学与工程学院,广东,广州,510640 3. 中山大学,数学与计算科学学院,广东,广州,510275 |
| 摘 要： | 针对训练大样本支持向量机内存开销大、训练速度慢的缺点，提出了一种改进的算法—边界邻近支持向量机。实验表明在分类效果相同情况下，改进算法训练速度明显提高。 |
| 关 键 词： | 统计学习理论 支持向量机 大样本 分类 |
| 文章编号： | 1001-3695(2006)04-0011-02 |
| 收稿时间： | 2005-03-21 |
| 修稿时间： | 2005-10-19 |
Boundary Nearest Support Vector Machines |
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| FENG Guo he,LI Yong jun,ZHU Si ming.Boundary Nearest Support Vector Machines[J].Application Research of Computers,2006,23(4):11-12. | |
| Authors: | FENG Guo he LI Yong jun ZHU Si ming |
| Affiliation: | (1.College of Economics Management, South China Normal University, Guangzhou University City, Guangzhou Guangdong 510006, China;2.School of Computer Science & Engineering, South China University of Technology, Guangzhou Guangdong 510640, China;3.School of Mathematics & Computational Science, Zhongshan University, Guangzhou Guangdong 510275, China) |
| Abstract: | Training a support vector machines on a data set of huge size exists one problem with slow training process. We use a modified support vector machines-boundary nearest support vector machines to resolve this problem and it speeds up the training process fastly comparing with conventional support vector machines under the same classification result. |
| Keywords: | Statistiacal Learning Theory Support Vector Machine Large-Scale Samples Classification |
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