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Conference Paper

Pattern Selection for Support Vector Classifiers

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84217

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

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Citation

Shin, H. (2002). Pattern Selection for Support Vector Classifiers. Intelligent Data Engineering and Automated Learning (IDEAL 2002), 97-103.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E074-1
Abstract
SVMs tend to take a very long time to train with a large data set. If "redundant" patterns are identified and deleted in pre-processing, the training time could be reduced significantly. We propose a k-nearest neighbors(k-NN) based pattern selection method. The method tries to select the patterns that are near the decision boundary and that are correctly labeled. The simulations over synthetic data sets showed promising results: (1) By converting a non-separable problem to a separable one, the search for an optimal error tolerance parameter became unnecessary. (2) SVM training time decreased by two orders of magnitude without any loss of accuracy. (3) The redundant SVs were substantially reduced.