Text categorization with support vector machines
| dc.contributor.author | Joachims, Thorsten | de |
| dc.date.accessioned | 2004-12-06T12:53:42Z | |
| dc.date.available | 2004-12-06T12:53:42Z | |
| dc.date.created | 1997 | de |
| dc.date.issued | 1999-10-29 | de |
| dc.description.abstract | This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning. The paper is written in English. | en |
| dc.format.extent | 258648 bytes | |
| dc.format.extent | 726397 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.issn | 0943-4135 | de |
| dc.identifier.uri | http://hdl.handle.net/2003/2595 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-5097 | |
| dc.language.iso | en | de |
| dc.publisher | Universität Dortmund | de |
| dc.relation.ispartofseries | Forschungsberichte des Lehrstuhls VIII, Fachbereich Informatik der Universität Dortmund ; 23 | de |
| dc.subject.ddc | 004 | de |
| dc.title | Text categorization with support vector machines | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | |
| dcterms.accessRights | open access |