Singapore: World Scientific Publishing Co. Pte. Ltd.
Abstract:
Support vector machines (SVMs) are a relatively recent machine learning technique. One of the SVM problems is that SVM is considerably slower in test phase caused by the large number of support vectors, which limits its practical use. To address this problem, we propose an artificial bee colony (ABC) algorithm to search for an optimal subset of the set of support vectors obtained through the training of the SVM, such that the original discriminant function is best approximated. Experimental results show that the proposed ABC algorithm outperforms some other compared methods in terms of the classification accuracy when the solution is reduced to the same size.
Relation:
International Journal of Pattern Recognition and Artificial Intelligence 26(8), 1250020(14pages)
