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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zha, Hongyuan Zhang, Congle Yu, Yong Xue, Gui-Rong |
| Abstract | Traditional Naive Bayes Classifier performs miserably on web-scale taxonomies. In this paper, we investigate the reasons behind such bad performance. We discover that the low performance are not completely caused by the intrinsic limitations of Naive Bayes, but mainly comes from two largely ignored problems: contradiction pair problem and discriminative evidence cancelation problem. We propose modifications that can alleviate the two problems while preserving the advantages of Naive Bayes. The experimental results show our modified Naive Bayes can significantly improve the performance on real web-scale taxonomies. |
| Starting Page | 1083 |
| Ending Page | 1084 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781605584874 |
| DOI | 10.1145/1526709.1526867 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-04-20 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Web-scale taxonomy Naive bayes |
| Content Type | Text |
| Resource Type | Article |
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