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    Learning with support vector machines for query-by-multiple-examples

    Zhang, Dell and Lee, W.S. (2008) Learning with support vector machines for query-by-multiple-examples. In: Oard, D.W. and Sebastiani, F. and Chua, T.-S. and Leong, M.-K. (eds.) Proceedings of the 31st Annual International ACM SIGIR conference on Research and Development in information Retrieval - SIGIR '08. ACM conference proceedings series 906. New York, U.S.: ACM Press, pp. 835-836. ISBN 9781605581644.

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    Abstract

    We explore an alternative 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. The effectiveness and efficiency of the proposed approaches have been confirmed by our experiments on four real-world datasets.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 30 May 2013 08:43
    Last Modified: 02 Dec 2016 13:26
    URI: http://eprints.bbk.ac.uk/id/eprint/7077

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