数据挖掘中的新方法——支持向量机

A New Method of Data Mining-Support Vector Machines

支持向量机是一种基于统计学习理论的新型机器学习方法,它采用了结构风险最小化原则来代替了经验风险最小化,能较好地解决小样本学习的问题;还采用核函数思想,把非线性空间的问题转换到线性空间,降低了算法的复杂度。正因为SVM有较完备的理论基础和较好的学习性能,在解决有限样本、非线性及高维模式识别问题中表现出许多特有的优势,成为当前机器学习领域的研究热点问题之一,并在很多领域都得到了成功的应用。

Support vector machine （SVM） is put forward by Vapnik, which is a new kind of machine learning method based on statistical learning theory. SVM solve small-sample problems by using structural risk minimization （SRM） to take the place of empirical risk minimization （ERM）. Moreover, nonlinear problems are changed into linear ones by using mapping the low dimension original space to high dimension feature space, and employing kernel function, which make the algorithm be realized easily. Because of such advantage, SVM become a hot spot of machine learning theory, and are applied successfully in many areas.