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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiu-Zhen Liang |
| Copyright Year | 2004 |
| Description | Author affiliation: Intelligence Comput. & Parallel Comput. Inst., Zhejiang Normal Univ., Jinhua, China (Jiu-Zhen Liang) |
| Abstract | This paper deals with support vector machine for multi-classification problem and its application to Web page document classification. Several multi-classifier of SVM are mentioned, their construction and computing complexity are compared and analyzed. For special application problems, most of multi-classifier of SVM are limited on the number of class. Web page document classification is a classical multi-classification problem; also the number of samples and the scale of dimension are so large that many classifiers are inefficient, such as multi-layer neural networks, RBF neural networks, k-neighbor, etc. SVM is the first choice for Web page document classification because of its advantage on non-effective of feature dimension scale. This paper focuses on direct design of multi-classifier of SVM and its application to Web page classification. The experiment results illustrate the efficiency of this kind of classifier. |
| Starting Page | 1347 |
| Ending Page | 1351 |
| File Size | 287688 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780384032 |
| DOI | 10.1109/ICMLC.2004.1381982 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-08-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Support vector machine classification Web pages Neural networks Dictionaries Feature extraction Frequency Multi-layer neural network Lungs Machine intelligence |
| Content Type | Text |
| Resource Type | Article |
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