Reducing SVM classification time using multiple mirror classifiers

Authors:
Jiun-Hung Chen;Chu-Song Chen
Affiliations:
Dept. of Comput. Sci. & Eng., Univ. of Washington, Seattle, WA, USA;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2004

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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. Experimental results show that our approach can successfully speed up SVM decisions while maintaining comparable classification accuracy.