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。
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.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2006年第2期106-110,共5页
Journal of National University of Defense Technology
关键词
支持向量机
机器学习
规则提取
知识获取
数据挖掘
support vector machines
maclfine learning
rule extraction
knowledge acquisition
data mining
