dc.date.accessioned | 2004-10-20T21:03:52Z | |
dc.date.accessioned | 2018-11-24T10:23:29Z | |
dc.date.available | 2004-10-20T21:03:52Z | |
dc.date.available | 2018-11-24T10:23:29Z | |
dc.date.issued | 2001-10-16 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/7241 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/7241 | |
dc.description.abstract | We 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. | en_US |
dc.format.extent | 14 p. | en_US |
dc.format.extent | 1240992 bytes | |
dc.format.extent | 1091543 bytes | |
dc.language.iso | en_US | |
dc.subject | AI | en_US |
dc.subject | text classification | en_US |
dc.subject | support vector machine | en_US |
dc.subject | multiclass classification | en_US |
dc.title | Improving Multiclass Text Classification with the Support Vector Machine | en_US |