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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yingsong Hu Qing He Dan Li |
| Copyright Year | 2011 |
| Abstract | The generalization ability of neural network is an important aspect affecting its application. Meanwhile, the selection of training samples has a great impact on this ability. In order to improve the completeness of training samples, a method of samples self-learning of BP neural network based on clustering is put forward in this paper. By using the method of clustering, new samples can be collected in network's practical application. Network will be more suitable for practical situation after retraining. This method is proved to be simple and creditable. Furthermore, concerned experiments show that this method has obvious effect in improving generalization ability of neural network. |
| Starting Page | 213 |
| Ending Page | 216 |
| File Size | 258716 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781612842899 |
| DOI | 10.1109/ICICTA.2011.63 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-03-28 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Computer science Accuracy Clustering algorithms Artificial neural networks Prediction algorithms clustering samples self-learning Indexes neural network |
| Content Type | Text |
| Resource Type | Article |
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