Support vector data description
Support vector data description
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DOI：
10.1023/b:mach.0000008084.60811.49
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发表时间：
2004-01-01
期刊：
影响因子：
7.5
通讯作者：
Duin, RPW
中科院分区：
文献类型：
--
作者：
Tax, DMJ;Duin, RPW
数据域描述涉及数据集的特征化。一个好的描述包括所有目标数据，但不包括多余的空间。数据集的边界可用于检测新数据或离群值。我们将提出支持向量数据描述（SVDD），这是受支持向量分类器的启发。它在数据集周围获得一个球形边界，类似于支持向量分类器，它可以通过使用其他核函数变得灵活。该方法对训练集中的离群值具有鲁棒性，并且能够通过使用负面示例来收紧描述。我们使用人工和真实的数据的支持向量数据描述的特点。
Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the Support Vector Data Descriptions using artificial and real data.