Support vector machines in relational databases
| dc.contributor.author | Rüping, Stefan | |
| dc.contributor.editor | Schubert, Sigrid E. | |
| dc.contributor.editor | Reusch, Bernd | |
| dc.contributor.editor | Jesse, Norbert | |
| dc.date.accessioned | 2019-11-28T09:31:24Z | |
| dc.date.available | 2019-11-28T09:31:24Z | |
| dc.date.issued | 2002 | |
| 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 implementation of the SVM learning algorithm, 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.identifier.isbn | 3-88579-348-2 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/30312 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Informatik bewegt: Informatik 2002 - 32. Jahrestagung der Gesellschaft für Informatik e.v. (GI) | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-19 | |
| dc.title | Support vector machines in relational databases | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 804 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 799 | |
| gi.conference.date | 30. September - 3. Oktober 2002 | |
| gi.conference.location | Dortmund | |
| gi.conference.sessiontitle | Regular Research Papers |
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