01542nas a2200193   4500008004100000245006100041210005800102260000900160300001400169490000600183520091700189653002601106653002101132653002601153653002401179100001401203700001401217856011701231       2009                        eng d00aQuery-by-multiple-examples using support vector machines0 aQuerybymultipleexamples using support vector machines  c2009  a202 - 2100 v73 aWe 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.10aInformation retrieval10aMachine learning10aSupportvector machine10aText classification1 aZhang, D.1 aLee, W.S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-77953247724&partnerID=40&md5=8fea0823a5d70e478bf228f315a5ef89