支持向量机理论与算法研究综述

An Overview on Theory and Algorithm of Support Vector Machines

  • 摘要: 统计学习理论(statistical learning theory,SLT)是一种小样本统计理论,着重研究在小样本情况下的统计规律及学习方法性质。支持向量机(support vector machinse, SVM)是一种基于SLT的新型的机器学习方法,由于其出色的学习性能,已经成为当前机器学习界的研究热点。 该文系统介绍了支持向量机的理论基础,综述了传统支持向量机的主流训练算法以及一些新型的学习模型和算法,最后指出了支持向量机的研究方向与发展前景。

     

    Abstract: Statistical learning theory is the statistical theory of smallsample, and it focuses on the statistical law and the nature of learning of small samples. Support vector machine is a new machine learning method based on statistical learning theory, and it has become the research field of machine learning because of its excellent performance. This paper describes the theoretical basis of support vector machines (SVM) systematically, sums up the mainstream machine training algorithms of traditional SVM and some new learning models and algorithms detailedly, and finally points out the research and development prospects of support vector machine.

     

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