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.
CITATION STYLE
Shin, H., & Cho, S. (2003). Fast pattern selection for support vector classifiers. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2637, pp. 376–387). Springer Verlag. https://doi.org/10.1007/3-540-36175-8_37
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