Making large scale SVM learning practical
| dc.contributor.author | Joachims, Thorsten | de |
| dc.date.accessioned | 2004-12-06T12:53:43Z | |
| dc.date.available | 2004-12-06T12:53:43Z | |
| dc.date.created | 1998 | de |
| dc.date.issued | 1999-10-29 | de |
| dc.description.abstract | Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples, off-the-shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVMLight is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SVMlight V2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains. Also published in: 'Advances in Kernel Methods - Support Vector Learning', Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola (eds.), MIT Press, Cambridge, USA, 1998. The paper is written in English. | en |
| dc.format.extent | 254496 bytes | |
| dc.format.extent | 723076 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.issn | 0943-4235 | de |
| dc.identifier.uri | http://hdl.handle.net/2003/2596 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-5098 | |
| dc.language.iso | en | de |
| dc.publisher | Universität Dortmund | de |
| dc.relation.ispartofseries | Forschungsberichte des Lehrstuhls VIII, Fachbereich Informatik der Universität Dortmund ; 24 | de |
| dc.subject.ddc | 004 | de |
| dc.title | Making large scale SVM learning practical | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | |
| dcterms.accessRights | open access |