序关系优化的多超平面排序学习模型  

Ranking Model of Optimized Multiple Hyperplanes Using Order Relations

作　　者：孙鹤立[1] 冯博琴[1] 黄健斌[2] 赵英良[1] 刘均[1] 

机构地区：[1]西安交通大学计算机科学与技术系,西安710049 [2]西安电子科技大学软件学院,西安710071

出　　处：《模式识别与人工智能》2010年第3期327-334,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家863计划项目(No.2008AA01Z131);陕西省自然科学基础研究计划项目(No.SJ08-ZT14)资助

摘　　要：在经典排序学习模型RankSVM的基础上,提出一种序关系优化的多超平面排序模型.该模型首先根据训练数据所属等级之间的序关系进行多个超平面的构建,然后将多个超平面得到的排序列表进行聚合获得最终的排序结果.在LETOR OHSUMED数据集上对所提出的模型进行实验测试,使用信息检索领域的多个经典指标对模型的性能进行评测,并与RankSVM等方法进行比较.实验结果显示该模型不仅获得更优的排序性能,而且能显著缩短训练时间.A ranking model utilizing the multiple hyperplanes optimized by the order relations is proposed based on RankSVM in this paper. Firstly, the multiple hyperplanes are built based on the order relations between the ranks for training data in this model. Then, the ranking list generated by multiple hyperplanes is aggregated to gain the final ranking results. The proposed model is tested on LETOR OHSUMED dataset, some typical indices in Information Retrieval field being applied to evaluate its performance and the method being compared with other methods such as RankSVM. The experimental results show that the model not only has better ranking performance but also shorten the training time evidently.

关 键 词：排序学习 多超平面 支持向量机(SVM) 序关系 排序聚合 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]