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A novel active learning method for security assessment of nonlinear systems is presented. The proposed method first extracts a dataset near the stability region boundary by using the direct method, and then learns a SVM model from the data. The constructed SVM classifier is shown to dramatically reduce the conservativeness of the estimated stability region and also to make a fast security assessment.