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Based on statistical learning theory, support vector machines (SVM) is a powerful tool for various classification problems, such as pattern recognition and speaker identification etc. However, training SVM consumes large memory and long computing time. This work proposes a cluster-based learning methodology to reduce training time and the memory size for SVM. By using k-means based clustering technique, training data at boundary of each cluster were selected for SVM learning. We also applied this technique to text-independent speaker identification problems. Without deteriorating recognition performance, the training data and time can be reduced up to 75% and 87.5% respectively.