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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tian Xia Yanmei Chai Tong Wang |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Information, Central University of Finance and Economics, Beijing, China (Yanmei Chai) || Department of Computer and Information Science, Shanghai Second Polytechnic University, Shanghai, China (Tian Xia; Tong Wang) |
| Abstract | Web contents are going overwhelming today. The numerous online documents, webpages, e-books, etc. are much useful but obtaining them is also time-consuming. Text categorization is one of the solutions to the issue. For all text categorization method, Support Vector Machines (SVM) is one of the most acceptable one. However, to perform more efficiently on webpages, it is necessary to add improvements on it. For webpages, the document title is meaningful as it is usually carefully created by editors and always shows the main content of the webpage. In this paper, an improvement of Support Vector Machine is proposed. The Document Representation for SVM emphases the features in documents' title which is always popular in webpages and obviously contains essential contextual information for the documents. |
| Starting Page | 110 |
| Ending Page | 113 |
| File Size | 122163 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467302418 |
| e-ISBN | 9781467302425 |
| DOI | 10.1109/ICCSE.2012.6295037 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-14 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Title Vector Text Classification Text categorization Support vector machine classification Educational institutions Vectors Natural language Processing SVM Kernel Support Vector Machines |
| Content Type | Text |
| Resource Type | Article |
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