Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Interactive document retrieval with relational learning
Proceedings of the 2001 ACM symposium on Applied computing
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Relevance Feedback using Support Vector Machines
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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This paper reports a new document retrieval method using non-relevant documents. Suppose, we need to find documents interesting to the user in as few iterations of human intervention as possible. In each iteration, a relatively small set of documents is evaluated in terms of the relevance to the user's interest. Ordinary relevance feedback needs both relevant and non-relevant documents, but the initial set of documents checked by the user may often not include relevant documents. Accordingly we propose a new feedback method using non-relevant documents only. This "non-relevance feedback" selects documents classified as "not non-relevant" and close to the boundary defined by the discriminant function obtained from one-class SVM. Experiments show that this method can efficiently retrieve a relevant documents.