Fast Reduction for Large-Scale Training Data Set

In order to cut down the time of training a large-scale data set by using SVM(support vector machine),a fast algorithm for reducing training sets was proposed based on class centroid.With this algorithm the most of non-support vectors are removed in the light of the geometrical distribution of samples.Experiments were made on several data sets at the level of 104 magnitude.The experimental results show that compared with the SMO(sequential minimal optimization) algorithm,the proposed algorithm decreases training time by 30% under the condition of ensuring the SVM’s classification accuracy to greatly improve SVM’s training speed.