Reducing SVM classification time using multiple mirror classifiers.
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.
Chen
Jiun-Hung
JH
Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA. jhchen@cs.washington.edu
Chen
Chu-Song
CS