A Learning Strategy of SVM Used to Large Training Set

This paper proposes a learning strategy of SVM used to large training set. First authors train an initial classifier with a small training set, then prune the large training set with the initial classifier to obtain a small reduction set. Training with the reduction set, final classifier is obtained. Experiments show that the learning strategy not only reduces the cost greatly but also obtains a classifier that has the same accuracy as(even better than) the classifier obtained by training large set directly. In addition, speed of classification is greatly improved.