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dc.contributor.authorJoachims, Thorstende
dc.date.accessioned2004-12-06T12:53:42Z-
dc.date.available2004-12-06T12:53:42Z-
dc.date.created1997de
dc.date.issued1999-10-29de
dc.identifier.issn0943-4135de
dc.identifier.urihttp://hdl.handle.net/2003/2595-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5097-
dc.description.abstractThis 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.extent258648 bytes-
dc.format.extent726397 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesForschungsberichte des Lehrstuhls VIII, Fachbereich Informatik der Universität Dortmund ; 23de
dc.subject.ddc004de
dc.titleText categorization with support vector machinesen
dc.typeTextde
dc.type.publicationtypereport-
dcterms.accessRightsopen access-
Appears in Collections:LS 08 Künstliche Intelligenz

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