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An One Class Classification Approach to Non-relevance Feedback Document Retrieval.
- Source :
- Fuzzy Systems & Knowledge Discovery (9783540283317); 2005, p1216-1225, 10p
- Publication Year :
- 2005
-
Abstract
- This paper reports a new document retrieval method using non-relevant documents. 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. The 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 retrieval is based on One-class Support Vector Machine. Our experimental results show that this method can retrieve relevant documents using information of non-relevant documents only. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540283317
- Database :
- Complementary Index
- Journal :
- Fuzzy Systems & Knowledge Discovery (9783540283317)
- Publication Type :
- Book
- Accession number :
- 32913764
- Full Text :
- https://doi.org/10.1007/11540007_161