Cooperative Clustering for Training SVMs

Support vector machines are currently very popular approaches to supervised learning. Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. This approach can also be used to reduce the number of support vectors in regression problems.