Data domain description using support vectors
Data domain description using support vectors
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发表时间：
1999
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通讯作者：
D. Tax;R. Duin
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作者：
D. Tax;R. Duin
． 本文介绍了一种新的数据域描述方法——支持向量域描述（SVDD），该方法的灵感来自于V.Vapnik的支持向量机。该方法计算一组物体周围体积最小的球形决策边界。该数据描述可用于新颖性或离群值检测。它包含了描述球面边界的支持向量，可以在不增加计算成本的情况下获得更高阶的边界描述。通过使用di（cid:11）事件核，该SVDD可以获得更灵活、更准确的数据描述。第一类（cid:12）的误差，即训练对象被拒绝的比例，可以立即从描述中估计出来。
. This paper introduces a new method for data domain description, inspired by the Support Vector Machine by V.Vapnik, called the Support Vector Domain Description (SVDD). This method computes a sphere shaped decision boundary with minimal volume around a set of objects. This data description can be used for novelty or outlier detection. It contains support vectors describing the sphere boundary and it has the possibility of obtaining higher order boundary descriptions without much extra computational cost. By using the di(cid:11)erent kernels this SVDD can obtain more (cid:13)exible and more accurate data descriptions. The error of the (cid:12)rst kind, the fraction of the training objects which will be rejected, can be estimated immediately from the description.