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    Query-by-multiple-examples using support vector machines

    Zhang, Dell and Sun Lee, W. (2009) Query-by-multiple-examples using support vector machines. Journal of Digital Information Management 7 (4), pp. 202-210. ISSN 0972-7272.

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    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.


    Item Type: Article
    Keyword(s) / Subject(s): Information retrieval, text classification, machine learning, Support Vector Machine
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 01 Feb 2011 11:56
    Last Modified: 09 Aug 2023 12:30


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