Support vector machine （SVM） is an important classi- fication tool in the pattern recognition and machine learning community, but its training is a time-consuming process. To deal with this problem, we propose a novel method to mine the useful information about classification hidden in the training sample for improving the training algorithm, and every training point is as- signed to a value that represents the classification information, respectively, where training points with the higher values are cho- sen as candidate support vectors for SVM training. The classifica- tion information value for a training point is computed based on the classification accuracy of an appropriate hyperplane for the training sample, where the hyperplane goes through the mapped target of the training point in feature space defined by a kernel fimction. Experimental results on various benchmark datasets show the effectiveness of our algorithm.
