Support Vector Data Description View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-01

AUTHORS

David M.J. Tax, Robert P.W. Duin

ABSTRACT

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. More... »

PAGES

45-66

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:mach.0000008084.60811.49

DOI

http://dx.doi.org/10.1023/b:mach.0000008084.60811.49

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023309467


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