Support Vector Data Description

被引：7

作者：

David M.J. Tax

Robert P.W. Duin

机构：

[1] Delft University of Technology,Pattern Recognition Group, Faculty of Applied Sciences

来源：

Machine Learning | 2004年 / 54卷

关键词：

outlier detection; novelty detection; one-class classification; support vector classifier; support vector data description;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：45 / 66

页数：21

共 27 条

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