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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Huifang Ma Zhixin Li Zhongzhi Shi |
| Copyright Year | 2010 |
| Abstract | The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification and link prediction. |
| Starting Page | 229 |
| Ending Page | 235 |
| File Size | 406939 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781769540122 |
| e-ISBN | 9781424466009 |
| DOI | 10.1109/APWeb.2010.27 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-04-06 |
| Publisher Place | Korea (South) |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computers Learning systems Programmable control Laboratories Web pages Information processing Predictive models Intelligent structures Data mining Adaptive control |
| Content Type | Text |
| Resource Type | Article |
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