A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is
Support Vector Machines (SVMs) have been adopted by many data-mining and information-retrieval applications for learning a mining or query concept, and then retrieving the "top-k" best matches to the concept. However, when the dataset is large, naively scanning the entire dataset to find the top matches is not scalable. In this work, we propose a kernel indexing strategy to substantially prune the search space and thus improve the performance of top-k queries. Our kernel indexer (KDX) takesdoi:10.1137/1.9781611972757.29 dblp:conf/sdm/PandaC05 fatcat:yyi7fbt6sjdadge45p2zxk72ki