Fast Pattern Selection for Support Vector Classifiers

Shin, H; Cho, S

doi:10.1007/3-540-36175-8_37

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Fast Pattern Selection for Support Vector Classifiers

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Shin, H., & Cho, S. (2003). Fast Pattern Selection for Support Vector Classifiers. In K.-Y. Whang, J. Jeon, K. Shim, & J. Srivastava (Eds.), Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference, PAKDD 2003, Seoul, Korea, April 30 – May 2 (pp. 376-387). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DC91-D

Abstract

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.