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SVM selective sampling for ranking with application to data retrieval

10 years 2 months ago
SVM selective sampling for ranking with application to data retrieval
Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vector Machines) - a classification and regression methodology - have also shown excellent performance in learning ranking functions. They effectively learn ranking functions of high generalization based on the "large-margin" principle and also systematically support nonlinear ranking by the "kernel trick". In this paper, we propose an SVM selective sampling technique for learning ranking functions. SVM selective sampling (or active learning with SVM) has been studied in the context of classification. Such techniques reduce the labeling effort in learning classification functions by selecting only the most informative samples to be labeled. However, they are not extendable to learning ranking functions, as the labeled data in ranking is relative ordering, or partial orders of data. Our propose...
Hwanjo Yu
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2005
Where KDD
Authors Hwanjo Yu
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