Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7241
Title: Improving Multiclass Text Classification with the Support Vector Machine
Keywords: AI
text classification
support vector machine
multiclass classification
Issue Date: 9-Oct-2013
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.
URI: http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers: AIM-2001-026
CBCL-210
http://hdl.handle.net/1721.1/7241
Appears in Collections:MIT Items

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.