document: 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 proposed sampling technique effectively learns an accurate SVM ranking function with fewer partial orders . We apply our sampling technique to the data retrieval application , which enables fuzzy search on relational databases by interacting with users for learning their preferences . Experimental results show a significant reduction of the labeling effort in inducing accurate ranking functions .
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