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A data reduction approach using hyperspherical sectors for support vector machine

In this paper, a new geometrical approach is proposed for data reduction to speed up the training of support vector machine (SVM) for large data sets. In this approach, the largest possible hyperspherical sector centered at each pattern and containing patterns of the same class is constructed, then the patterns that tend to be far away from the decision boundary are removed from the training set. Experiments show the effectiveness of the proposed approach in speeding up the training process while maintaining the same level of accuracy as the standard SVM.