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Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7241
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dc.creatorRennie, Jason D. M.-
dc.creatorRifkin, Ryan-
dc.date2004-10-20T21:03:52Z-
dc.date2004-10-20T21:03:52Z-
dc.date2001-10-16-
dc.date.accessioned2013-10-09T02:48:39Z-
dc.date.available2013-10-09T02:48:39Z-
dc.date.issued2013-10-09-
dc.identifierAIM-2001-026-
dc.identifierCBCL-210-
dc.identifierhttp://hdl.handle.net/1721.1/7241-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionWe compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM's lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties.-
dc.format14 p.-
dc.format1240992 bytes-
dc.format1091543 bytes-
dc.formatapplication/postscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAIM-2001-026-
dc.relationCBCL-210-
dc.subjectAI-
dc.subjecttext classification-
dc.subjectsupport vector machine-
dc.subjectmulticlass classification-
dc.titleImproving Multiclass Text Classification with the Support Vector Machine-
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