- 期刊
- OpenAccess
Support Vector Domain Description with Maximum Between Spheres Separability
並列摘要
Support Vector Domain Description (SVDD) is inspired by the Support Vector Classifier. It obtains a sphere shaped decision boundary with minimal volume around a dataset. This data description can be used for novelty or outlier detection. Our approach is always to minimize the volume of the sphere describing the dataset, while at the same time maximize the separability between the spheres. To build such sphere we only need to solve a convex quadratic optimization problem that can be efficiently solved with the existing software packages, simulation results on seventeen benchmark datasets have successfully validated the effectiveness of the proposed method.
被引用紀錄
林玟君（2015）。台灣代理孕母合法化之探討〔碩士論文，國立中正大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0033-2110201614032202
延伸閱讀
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