| dc.creator |
Rennie, Jason D. M. |
|
| dc.creator |
Rifkin, Ryan |
|
| dc.date |
2004-10-20T21:03:52Z |
|
| dc.date |
2004-10-20T21:03:52Z |
|
| dc.date |
2001-10-16 |
|
| dc.date.accessioned |
2013-10-09T02:48:39Z |
|
| dc.date.available |
2013-10-09T02:48:39Z |
|
| dc.date.issued |
2013-10-09 |
|
| dc.identifier |
AIM-2001-026 |
|
| dc.identifier |
CBCL-210 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/7241 |
|
| dc.identifier.uri |
http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
|
| dc.description |
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. |
|
| dc.format |
14 p. |
|
| dc.format |
1240992 bytes |
|
| dc.format |
1091543 bytes |
|
| dc.format |
application/postscript |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.relation |
AIM-2001-026 |
|
| dc.relation |
CBCL-210 |
|
| dc.subject |
AI |
|
| dc.subject |
text classification |
|
| dc.subject |
support vector machine |
|
| dc.subject |
multiclass classification |
|
| dc.title |
Improving Multiclass Text Classification with the Support Vector Machine |
|