基于聚类-二叉树支持向量机的砂土液化预测模型  

Support vector machine model for predicting sand liquefaction based on clustering binary tree algorithm

作　　者：刘勇健[1] 

机构地区：[1]广东工业大学,广州510006

出　　处：《岩土力学》2008年第10期2764-2768,共5页Rock and Soil Mechanics

基　　金：广东省自然科学基金项目(No.6021462);广东省重点学科基金(No.2005D16)资助

摘　　要：建立在统计学习理论基础之上的支持向量机(SVM),是一种基于结构风险最小的小样本机器学习方法。经典的支持向量机主要针对二分类问题,而工程实践中遇到的往往是多分类问题。根据影响砂土液化的主要因素,采用聚类分析中的类距离思想,建立了基于聚类-二叉树的多类支持向量机的砂土液化判别模型。该模型可以通过有限样本的学习,建立砂土液化与各影响因素之间的非线性关系。研究结果表明,基于聚类-二叉树支持向量机的层次结构合理,分类精度高,泛化性好,可对砂土液化等级进行较准确判别。Support vector machine （SVM） is a novel and powerful learning method which is derived based on statistical learning theory （SLT） and the structural risk minimization principle. Traditional SVM only deals with the binary classification problems, however, there are large numbers of multi-classification problems in practical engineering. With a view to the main factors with important influence on sand liquefaction, multi-class support vector machines model based on clustering-binary tree is established. The nonlinear relationship between sand liquefaction and influencing factors is obtained from the finite empirical data by SVM. The results indicate that the structure of model base on SVM is reasonable; this algorithm is feasible; and it can predict the sand liquefaction more accurately.

关 键 词：支持向量机 砂土液化 聚类 二叉树 统计学习 预测模型 

分 类 号：TB115[理学—数学]