Support Vector Machines Based Active Learning for the Relevance Feedback Document Retrieval
Support Vector Machines Based Active Learning for the Relevance Feedback Document Retrieval
复制标题
基于支持向量机的相关反馈文档检索主动学习
DOI：
10.1109/wi-iatw.2006.125
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
S. Yamada
中科院分区：
文献类型：
--
作者：
T. Onoda;H. Murata;S. Yamada
本文描述了 SVM（支持向量机）在使用主动学习的交互式文档检索中的应用。将SVM等分类学习应用到相关性反馈中已经做了一些工作，并取得了成功的结果。然而他们在文档检索中并没有充分利用样本分布的特点。我们提出了启发式方法，根据文档检索中示例的分布来偏置文档显示。这种启发式方法是通过选择示例来向用户展示正支持向量的邻居，从而提高学习效率。我们使用我们提出的启发式实现了基于 SVM 的交互式文档检索系统，并将其与传统系统（如基于 Rocchio 的系统和没有启发式的基于 SVM 的系统）进行了比较。我们使用包括超过 500,000 篇论文的大型数据集进行了系统实验，并证实我们的系统优于其他系统
This paper describes an application of SVM (support vector machines) to interactive document retrieval using active learning. Some works have been done to apply classification learning like SVM to relevance feedback and obtained successful results. However they did not fully utilize characteristic of example distribution in document retrieval. We propose heuristics to bias document showing according to distribution of examples in document retrieval. This heuristic is executed by selecting examples to show a user in neighbors of positive support vectors, and it improves learning efficiency. We implemented a SVM-based interactive document retrieval system using our proposed heuristic, and compare it with conventional systems like Rocchio-based system and a SVM-based system without the heuristic. We conducted systematic experiments using large data sets including over 500,000 paper articles and confirmed our system outperformed other ones
DOI：
--
发表时间：
2001-06
期刊：
--
影响因子：
--
作者：
H. Drucker;B. Shahraray;D. Gibbon
通讯作者：
H. Drucker;B. Shahraray;D. Gibbon