Regression Depth and Support Vector Machine

dc.contributor.author	Christmann, Andreas	de
dc.date.accessioned	2004-12-06T18:51:30Z
dc.date.available	2004-12-06T18:51:30Z
dc.date.issued	2004	de
dc.description.abstract	The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable. Christmann and Rousseeuw [CR01] showed that RDM is also useful for the case of binary regression. Vapnik’s convex risk minimization principle [Vap98] has a dominating role in statistical machine learning theory. Important special cases are the support vector machine (SVM), epsilon-support vector regression and kernel logistic regression. In this paper connections between these methods from different disciplines are investigated for the case of pattern recognition. Some results concerning the robustness of the SVM and other kernel based methods are given.	en
dc.format.extent	650223 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/5306
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6818
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.subject.ddc	000	de
dc.title	Regression Depth and Support Vector Machine	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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