Support vector data description

作     者：Tax, DMJ Duin, RPW 

作者机构：Delft Univ Technol Fac Appl Phys Pattern Recognit Grp NL-2628 CJ Delft Netherlands 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：2004年第54卷第1期

页      面：45-66页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Dutch Organization for Scientific Research Foundation for Applied Sciences Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO Stichting voor de Technische Wetenschappen, STW 

主　　题：outlier detection novelty detection one-class classification support vector classifier support vector data description 

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.