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2013 IEEE 13th International Conference on Data Mining (2001)
San Jose, California
Nov. 29, 2001 to Dec. 2, 2001
ISBN: 0-7695-1119-8
pp: 589
ABSTRACT
<p>SVMs suffer from the problem of large memory requirement and CPU time when trained in batch mode on large data sets. We overcome these limitations, and at the same time make SVMs suitable for learning with data streams, by constructing incremental learning algorithms.</p> <p>We first introduce and compare different incremental learning techniques, and show that they are capable of producing performance results similar to the batch algorithm, and in some cases superior condensation properties. We then consider the problem of training SVMs using stream data. Our objective is to maintain an updated representation of recent batches of data. We apply incremental schemes to the problem and show that their accuracy is comparable to the batch algorithm.</p>
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CITATION
Dimitrios Gunopulos, Carlotta Domeniconi, "Incremental Support Vector Machine Construction", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 589, 2001, doi:10.1109/ICDM.2001.989572
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