摘要
支持向量机(support vector machines,SVM)是一种基于小样本统计理论的机器学习方法,在解决非线性及高维模式识别中表现出明显的优势,是近年来机器学习领域的研究热点。文中介绍了支持向量机的原理,对经典的训练算法和一些新型的学习模型进行了阐述,最后指出所面临的问题和研究方向。
Support vector machines（SVM）is a new machine learning method based on the statistical theory of small sample, and it has the obvious advantage in solving nonlinear and high-dimensional pattern recognition. In recent years, SVM has become a hot research field of machine learning. This paper describes the principle of support vector machines, sums up the classic training algorithms of SVM and some new learning models detailedly, and finally points out some problems and research direction of support vector machines.
出处
《信息技术》
2013年第9期152-154,159,共4页
Information Technology
