支持向量机及其在地震预报中的应用前景  

Support Vector Machines and Its Application Future in Earthquake Predication

作　　者：王炜[1] 林命週[1] 马钦忠[1] 赵利飞[1] 

机构地区：[1]上海市地震局,上海200062

出　　处：《西北地震学报》2006年第1期78-84,共7页Northwestern Seismological Journal

基　　金：地震科学联合基金(104090)

摘　　要：统计学习理论(SLT)是研究小样本情况下机器学习规律的理论。支持向量机(SVM)基于统计学习理论,可以处理高度非线性分类和回归等问题,不但较好地解决了小样本、过学习、高维数、局部最小等实际难题,而且具有很强的泛化(预测)能力。本文介绍了支持向量机的分类、回归方法,分析了这一方法的特点,讨论了该方法在地震预报中的应用前景。Statistical learning theory （SLT） is a small-sample statistics theory. Support vector machine （SVM） is a new machine learning method based on statistical learning theory. It can process the high nonlinear problems with classification and regression, SVM not only can solve some problems, such as small-sampler over-fitting, high-dimension and local minimum, but also has higher generalization （forecasting） ability than that of the artificial neural networks. In this paper, the classification and regression methods of SVM are introduced , the characters of the methods are analyzed , and the application future of SVM in earthquake prediction is discussed also.

关 键 词：统计学习理论 支持向量机 分类 回归 地震预报 

分 类 号：P315.75[天文地球—地震学]