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|Title:||Learning with support vector machines for query-by-multiple-examples||Authors:||Dell, Z.
Support vector machine
|Issue Date:||2008||Citation:||Dell, Z.,Lee, W.S. (2008). Learning with support vector machines for query-by-multiple-examples. ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings : 835-836. ScholarBank@NUS Repository. https://doi.org/10.1145/1390334.1390529||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.||Source Title:||ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings||URI:||http://scholarbank.nus.edu.sg/handle/10635/40320||ISBN:||9781605581644||DOI:||10.1145/1390334.1390529|
|Appears in Collections:||Staff Publications|
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