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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shuguang Zhu Fangzhou Zhu Weibing Fan Qian Mo Zhiqiang Li Xiaofeng Hu Guangya Si |
| Copyright Year | 2010 |
| Abstract | A series of support vector machine (SVM) forecast experiments are carried out to reveal the relation between the SVM training sample size and SVM correct forecast ratio for simulation experiment results. Experiment results show that the SVM correct forecast ratio increases to some extent with the number of training samples becoming more and then keeps unchanged even if the SVM training sample number increases further. And SVM has also been proved to be able to overcome the over-fitting issue always afflicting Back-Propagation Neural Networks (BPNN). |
| Starting Page | 138 |
| Ending Page | 141 |
| File Size | 251155 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424472796 |
| e-ISBN | 9781424472802 |
| DOI | 10.1109/ICICTA.2010.301 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-11 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer simulation training sample size Predictive models Support vector machines Design engineering Intelligent networks forecast experiment support vector machine Support vector machine classification Technology forecasting Sampling methods Computer networks correct forecast ratio Testing |
| Content Type | Text |
| Resource Type | Article |
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