| 引用本文: | 王强,沈永平,陈英武.支持向量机规则提取.[J].国防科技大学学报,2006,28(2):106-110.[点击复制] |
| WANG Qiang,SHEN Yongping,CHEN Yingwu.Rule Extraction from Support Vector Machines[J].Journal of National University of Defense Technology,2006,28(2):106-110[点击复制] |
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| 支持向量机规则提取 |
| 王强, 沈永平, 陈英武 |
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(国防科技大学 信息系统与管理学院,湖南 长沙 410073)
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| 摘要: |
| 支持向量机是一种黑箱模型，其学习到的知识蕴含在决策函数中，不仅影响了用户对利用支持向量机技术构建智能系统的信心，还阻碍了支持向量机技术在数据挖掘领域的应用。由于对支持向量机规则提取进行研究有助于解决上述问题，因此该领域正成为机器学习和智能计算界的研究热点。分析了具有代表性的支持向量机规则提取算法，并提出该领域未来的研究重点。 |
| 关键词: 支持向量机 机器学习 规则提取 知识获取 数据挖掘 |
| DOI： |
| 投稿日期：2005-09-20 |
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| Rule Extraction from Support Vector Machines |
| WANG Qiang, SHEN Yongping, CHEN Yingwu |
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(College of Information System and Management,National Univ. of Defense Technology,Changsha 410073, China)
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| Abstract: |
| Support vector machines is a blackbox model whose knowledge is concealed in the decision function. This has not only weakened the confidence of users in building intelligent systems using support vector machines techniques, but also hindered the application of support vector machines to data mining. Since extracting rules from support vector machines help to solve those problems, this area is becoming a hot topic in both machine learning and intelligent computing communities. In this paper, the typical algorithms for rule extraction from support vector machines are introduced, and some issues valuable for future exploration in this area are indicated. |
| Keywords: support vector machines machine learning rule extraction knowledge acquisition data mining |
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