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  1. Proceedings of the 2nd international workshop on Computer vision meets databases (CVDB '05)
  2. Using pivots to index for support vector machine queries
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Using pivots to index for support vector machine queries

Content Provider ACM Digital Library
Author Qamra, Arun Chang, Edward Y.
Abstract In many data-mining applications, Support Vector Machines are used to learn query concepts, and then the learned SVM is used to find the corresponding best matches in a given dataset. When the dataset is large, naively scanning the entire dataset to find the instances with the highest classification scores is not practical. An indexing strategy is thus desirable for scalability. In contrast to queries in traditional similarity search scenarios which are in the form of an input space point, SVM queries are hyperplanes in a (kernel function induced) feature space, and the best matches are instances farthest from the hyperplane. Also, the kernel parameters used, and hence the feature space used, may vary with the query. These issues make the problem challenging. In this work, we propose an indexing strategy that uses pivots (selected using PCA or KPCA) to prune irrelevant instances from the dataset, and zoom in on a smaller candidate set, to efficiently answer SVM queries.
Starting Page 59
Ending Page 64
Page Count 6
File Format PDF
ISBN 1595931511
DOI 10.1145/1160939.1160954
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-06-17
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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