Reducing SVM classification time using multiple mirror classifiers

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Jiun-Hung Chen, Chu-Song Chen

Abstract

We propose an approach that uses mirror point pairs and a multiple classifier system to reduce the classification time of a support vector machine (SVM). Decisions made with multiple simple classifiers formed from mirror pairs are integrated to approximate the classification rule of a single SVM. A coarse-to-fine approach is developed for selecting a given number of member classifiers. A clustering method, derived from the similarities between classifiers, is used for a coarse selection. A greedy strategy is then used for fine selection of member classifiers. Selected member classifiers are further refined by finding a weighted combination with a perceptron. Experiment results show that our approach can successfully speed up SVM decisions while maintaining comparable classification accuracy.

References

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Jun 1, 1997·Neural Networks : the Official Journal of the International Neural Network Society·Sherif Hashem
Feb 5, 2008·IEEE Transactions on Neural Networks·K R MüllerB Schölkopf
Feb 5, 2008·IEEE Transactions on Neural Networks·Jiun-Hung Chen, Chu-Song Chen
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Citations

Feb 16, 2013·Journal of Digital Imaging·Jitendra VirmaniNiranjan Khandelwal
Jan 11, 2011·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society· Si Si Kwok-Ping Chan
May 12, 2010·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Yanwu XuHong Qiao

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