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Fast Pattern Selection for Support Vector Classifiers

MPG-Autoren
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Shin,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Shin, H. (2003). Fast Pattern Selection for Support Vector Classifiers. Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference (PAKDD 2003), 376-387.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DC91-D
Zusammenfassung
Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any loss in classification accuracies.