Non-Relevance Feedback Document Retrieval based on One Class SVM and SVDD
Non-Relevance Feedback Document Retrieval based on One Class SVM and SVDD
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DOI：
10.1109/ijcnn.2006.246829
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
T. Onoda;H. Murata;S. Yamada
中科院分区：
文献类型：
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作者：
T. Onoda;H. Murata;S. Yamada
本文提出了一种利用非相关文档进行文档检索的新方法。特别是，本文报告的检索效率之间的比较一类支持向量机（SVM）的基础上，支持向量数据描述（SVDD）的交互式文档检索方法使用非相关文档。从大量的文档数据集中，我们需要在尽可能少的人工测试或检查迭代中找到与人类感兴趣相关的文档。在每次迭代中，评估一小批与人类兴趣相关的文档。我们采用基于支持向量机的主动学习技术来评估连续的批次，这被称为相关反馈。我们提出的方法已经非常有用的文档检索与相关反馈实验。传统的相关性反馈需要一组相关和非相关的文档才能有效地工作。然而，显示给用户的初始检索文档有时不包括相关文档。为了解决这个问题，我们提出了一种新的反馈方法，只使用非相关文档的信息。我们把这种方法称为非相关反馈文档检索。非相关反馈文档检索是基于一类支持向量机和支持向量数据描述的。我们的实验结果表明，一类支持向量机的方法可以有效地检索相关的文档，仅使用非相关文档的信息。
This paper reports a new document retrieval method using non-relevant documents. Especially, this paper reports a comparison of retrieval efficiency between one class support vector machine (SVM) based and support vector data description (SVDD) based interactive document retrieval method using non-relevant documents only. From a large data set of documents, we need to find documents that relate to human interesting in as few iterations of human testing or checking as possible. In each iteration a comparatively small batch of documents is evaluated for relating to the human interesting. We applied active learning techniques based on support vector machine for evaluating successive batches, which is called relevance feedback. Our proposed approach has been very useful for document retrieval with relevance feedback experimentally. The traditional relevance feedback needs a set of relevant and non-relevant documents to work usefully. However, the initial retrieved documents, which are displayed to a user, sometimes don't include relevant documents. In order to solve this problem, we propose a new feedback method using information of non-relevant documents only. We named this method non-relevance feedback document retrieval. The non-relevance feedback document retrievals are based on one class support vector machine and support vector data description. Our experimental results show that one class support vector machine based method can retrieve relevant documents efficiently using information of non-relevant documents only.