Support Vector Data Description

Support Vector Data Description. Tax, D. M. & Duin, R. P. Machine Learning, 54(1):45–66, January, 2004. 02344

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.

@article{tax_support_2004,
	title = {Support {Vector} {Data} {Description}},
	volume = {54},
	issn = {0885-6125},
	url = {http://link.springer.com/10.1023/B:MACH.0000008084.60811.49},
	doi = {10.1023/B:MACH.0000008084.60811.49},
	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.},
	language = {en},
	number = {1},
	urldate = {2019-06-24},
	journal = {Machine Learning},
	author = {Tax, David M.J. and Duin, Robert P.W.},
	month = jan,
	year = {2004},
	note = {02344},
	pages = {45--66}
}

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