摘要
支持向量机是一种新型机器学习方法,能较好地解决小样本、非线性、高维数和局部极小点等实际问题,对未来样本有较好的泛化能力,已成为当前机器学习界的研究热点。本文介绍了支持向量机的数学理论基础及其研究现状,并介绍了支持向量机实用算法的研究情况,指出了支持向量机的局限性和未来的研究方向。
Sopport Vector Machines （SVM）are a new kind of novel machine learning methods, based on statistical learning theory,which have become the hotspot of machine learning because of their excellent learning performance. The method of support vector machines has been developed for solving classificationand regression problems. In this paper,the mathemati- cal foundation of SVM and the status in quo are introduced,and several applied algorithms are presented. Some limitations and future research issues are also discussed.
出处
《河南科技学院学报》
2007年第4期89-92,共4页
Journal of Henan Institute of Science and Technology(Natural Science Edition)
基金
河南省科技发展计划项目(0624420016)
河南省教育厅科技攻关项目(2007150018)
关键词
机器学习
统计学习理论
支持向量机
模式识别
machine learning
statistical learning theory
support vector machines
pattern regeognition
