Support Vector Machines in Relational Databases

dc.contributor.author	Rüping, Stefan	de
dc.date.accessioned	2004-12-06T18:49:54Z
dc.date.available	2004-12-06T18:49:54Z
dc.date.issued	2002	de
dc.description.abstract	Today, most of the data in business applications is stored in relational databases. Relational database systems are so popular, because they offer solutions to many problems around data storage, such as efficiency, effectiveness, usability, security and multi-user support. To benefit from these advantages in Support Vector Machine (SVM) learning, we will develop an SVM implementation that can be run inside a relational database system. Even if this kind of implementation obviously cannot be as efficient as a standalone implementation, it will be favorable in situations, where requirements other than efficiency for learning play an important role.	en
dc.format.extent	74321 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/5211
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15188
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	support vector machines	en
dc.subject	efficiency	en
dc.subject.ddc	310	de
dc.title	Support Vector Machines in Relational Databases	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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