A Simple Method for Estimating Conditional Probabilities for SVMs

dc.contributor.author	Rüping, Stefan	de
dc.date.accessioned	2004-12-06T18:51:32Z
dc.date.available	2004-12-06T18:51:32Z
dc.date.issued	2004	de
dc.description.abstract	Support Vector Machines (SVMs) have become a popular learning algorithm, in particular for large, high-dimensional classification problems. SVMs have been shown to give most accurate classification results in a variety of applications. Several methods have been proposed to obtain not only a classification, but also an estimate of the SVMs confidence in the correctness of the predicted label. In this paper, several algorithms are compared which scale the SVM decision function to obtain an estimate of the conditional class probability. A new simple and fast method is derived from theoretical arguments and empirically compared to the existing approaches.	en
dc.format.extent	110953 bytes
dc.format.extent	89536 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5308
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15242
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.subject.ddc	000	de
dc.title	A Simple Method for Estimating Conditional Probabilities for SVMs	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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