基于最小二乘支持向量机的网页主题语义分类的研究  

ON SEMANTIC CLASSIFICATION OF WEBPAGE SUBJECTS BASED ON LEAST SQUARES SUPPORT VECTOR MACHINES

作　　者：樊中华[1] 侯占斌[2] 张晨星[2] 马骁[2] 

机构地区：[1]北京科技大学信息工程学院,北京100083 [2]北京航空航天大学计算机学院,北京100083

出　　处：《计算机应用与软件》2009年第12期53-55,59,共4页Computer Applications and Software

基　　金：国家"十一五"科技支撑计划项目(2006BAK11B03)

摘　　要：提出了对网页主题进行语义扩展的方法,利用最小二乘支持向量机LSSVM(least squares support vector machines)来代替传统的支持向量机SVM(support vector machine)的分类技术。在建立LSSVM模型的多类别分类算法基础上,将其应用到网页主题语义分类。实验表明,最小二乘支持向量机学习速度快,在小样本情况下具有良好的非线性建模和泛化能力,对网页主题语义分类具有很好的效果。This paper proposes a new method of semantic extension for webpage subjects by utilizing the least squares support vector machines （LSSVM） to replace the classification technique with traditional support vector machine （SVM）. On the basis of setting up the multiclass classification algorithm of LSSVM model, the authors applied the method to semantic classification of webpage subjects. The experimental results show that LSSVM model has some advantages such as fast learning speed, nonlinearity modelling and generalization power in condition of small number of examples, and has good performance on semantic classification of webpage subjects.

关 键 词：最小二乘支持向量机 语义建模 网页主题语义分类 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置] TP311.13[自动化与计算机技术—控制科学与工程]