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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sun, Shu Liu, Fang Liu, Jun Dou, Yinan Yu, Hua |
| Copyright Year | 2014 |
| Abstract | In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. These deep learning approaches have been applied to image recognition, voice recognition and text processing. However, to our knowledge, the deep learning approaches have not been extensively studied for web data. In this paper, we apply deep belief networks to web data and evaluate the algorithm on various classification experiments by comparing its performance with that of the SVM classification algorithm. In addition, the experiments show good performance of the deep belief networks for various classification tasks. |
| Starting Page | 768 |
| Ending Page | 773 |
| File Size | 289485 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479979813 |
| DOI | 10.1109/CSE.2014.158 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-12-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training Algorithm design and analysis DBN Accuracy Training data Vectors SVM Electronic commerce Deep Learning Web Classification |
| Content Type | Text |
| Resource Type | Article |
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