| 內容摘要 | Abstract- In this paper, we propose a novel approach to generate artificial outliers for support vector data paper1list_description3 with boundary value method. In SVDD, the width parameter S and the penalty parameter C influence the learning results. The N-fold M times cross-validation is well-known and popular scheme th calculate the best (C, s) values. To auomatically optimize the identification rate, we need more outliers. Due to this reason, we utilize boundary value in any two dimensions randomly to generalize new outliers. At the last, we use three benchmar data sets: Iris, WIne, and Balance-scale data base to validate the approach in this research has better calssification result and faster perfoumance. |