适应Web检索的平滑型排序支持向量机  

Smooth Ranking Support Vector Machine Adapting to Web Retrieval

作　　者：何海江[1] 

机构地区：[1]长沙学院计算机科学与技术系,长沙410003

出　　处：《模式识别与人工智能》2009年第6期891-897,共7页Pattern Recognition and Artificial Intelligence

基　　金：湖南省自然科学基金项目(No.06JJ2065);湖南省教育厅科学研究项目(No.09C123)资助

摘　　要：代价敏感的排序支持向量机将样本的排序问题转换为样本对的分类问题,以适应Web信息检索.然而急剧膨胀的训练样本对使得学习时间过长.为此,文中提出一种支持二次误差的代价敏感的平滑型排序支持向量机(cs-sRSVM),用分段多项式光滑函数近似铰链损失函数,将优化目标转变为无约束问题.再由Newton-YUAN算法求无约束问题的唯一最优解.在排序学习公开数据集LETOR的实验表明,cs-sRSVM与已有的代价敏感排序算法相比,训练时间更短,而检索性能同样出色.Cost-sensitive ranking support vector machine converts the order relation of samples into the classification relation of sample pairs, and it is particularly well suited to web information retrieval. However, learning large amounts of sample pairs takes extremely long time. A cost-sensitive smooth ranking support vector machine （cs-sRSVM） using 2-Norm error is presented. Firstly, the optimization object is transformed into unconstrained problem. Secondly, the smooth piecewise polynomial function is approximated to the hinge loss function. Finally, the unique optimal solution is obtained by applying Newton-YUAN method. The experimental results on a public dataset LETOR show that the training time of cs-sRSVM is faster than that of the existing cost-sensitive ranking algorithm, and its retrieval performance is equally impressive.

关 键 词：代价敏感 排序支持向量机(RSVM) 二次误差 信息检索 平滑 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]