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Title: Query-by-multiple-examples using support vector machines
Authors: Zhang, D.
Lee, W.S. 
Keywords: Information retrieval
Machine learning
Supportvector machine
Text classification
Issue Date: 2009
Source: Zhang, D.,Lee, W.S. (2009). Query-by-multiple-examples using support vector machines. Journal of Digital Information Management 7 (4) : 202-210. ScholarBank@NUS Repository.
Abstract: We identify and explore an Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information need is described not by a set of terms but by a set of documents. Intuitive ideas for QBME include using the centroid of these documents or the well-known Rocchio algorithm to construct the query vector. We consider this problem from the perspective of text classification, and find that a better query vector can be obtained through learning with Support Vector Machines (SVMs). For online queries, we show how SVMs can be learned from one-class examples in linear time. For offline queries, we show how SVMs can be learned from positive and unlabeled examples together in linear or polynomial time, optimising some meaningful multivariate performance measures. The effectiveness and efficiency of the proposed approaches have been confirmed by our experiments on four real-world datasets.
Source Title: Journal of Digital Information Management
ISSN: 09727272
Appears in Collections:Staff Publications

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