Consistency and robustness of kernel based regression

dc.contributor.author	Christmann, Andreas	de
dc.contributor.author	Steinwart, Ingo	de
dc.date.accessioned	2005-01-31T08:15:41Z
dc.date.available	2005-01-31T08:15:41Z
dc.date.issued	2005	de
dc.description.abstract	We investigate properties of kernel based regression (KBR) methods which are inspired by the convex risk minimization method of support vector machines. We first describe the relation between the used loss function of the KBR method and the tail of the response variable Y . We then establish a consistency result for KBR and give assumptions for the existence of the influence function. In particular, our results allow to choose the loss function and the kernel to obtain computational tractable and consistent KBR methods having bounded influence functions. Furthermore, bounds for the sensitivity curve which is a finite sample version of the influence function are developed, and some numerical experiments are discussed.	de
dc.format.extent	381952 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/20093
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15685
dc.language.iso	en	de
dc.subject.ddc	310	de
dc.title	Consistency and robustness of kernel based regression	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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