Relevance Feedback using Support Vector Machines
Relevance Feedback using Support Vector Machines
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发表时间：
2001-06
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通讯作者：
H. Drucker;B. Shahraray;D. Gibbon
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作者：
H. Drucker;B. Shahraray;D. Gibbon
我们证明，在文本文档信息检索 (IR) 的相关性反馈 (RF) 环境中，支持向量机 (SVM) 比传统算法要好得多。我们跟踪性能作为反馈迭代的函数，并表明，如果搜索的主题在数据库中具有较高的可见性，则传统算法在初始反馈迭代中表现非常好，但如果相关文档只占数据库的一小部分，则它们的表现非常差。然而，当初步搜索中返回的文档数量较少且相关文档数量较少时，SVM 的表现非常好。所检查的竞争算法是 Rocchio、Ide Regular 和 Ide dec-hi。
We show that support vectors machines (SVM’s) are much better than conventional algorithms in a relevancy feedback (RF) environment in information retrieval (IR) of text documents. We track performance as a function of feedback iteration and show that while the conventional algorithms do very well in the initial feedback iteration if the topic searched for has high visibility in the data base, they do very poorly if the relevant documents are a small percentage of the data base. SVM’s however do very well when the number of documents returned in the preliminary search is low and the number of relevant documents is small. The competitive algorithms examined are Rocchio, Ide regular, and Ide dec-hi.